FedLab provides the necessary modules for FL simulation, including communication, compression, model optimization, data partition and other functional modules. Users can build FL simulation environment with custom modules like playing with LEGO bricks.

Overview of FedLab#

Introduction#

Federated learning (FL), proposed by Google at the very beginning, is recently a burgeoning research area of machine learning, which aims to protect individual data privacy in distributed machine learning process, especially in finance, smart healthcare and edge computing. Different from traditional data-centered distributed machine learning, participants in FL setting utilize localized data to train local model, then leverages specific strategies with other participants to acquire the final model collaboratively, avoiding direct data sharing behavior.

To relieve the burden of researchers in implementing FL algorithms and emancipate FL scientists from repetitive implementation of basic FL setting, we introduce highly customizable framework FedLab in this work. FedLab is builded on the top of torch.distributed modules and provides the necessary modules for FL simulation, including communication, compression, model optimization, data partition and other functional modules. FedLab users can build FL simulation environment with custom modules like playing with LEGO bricks. For better understanding and easy usage, FL algorithm benchmark implemented in FedLab are also presented.

For more details, please read our full paper.

Overview#

FedLab provides two basic roles in FL setting: Server and Client. Each Server/Client consists of two components called NetworkManager and ParameterHandler/Trainer.

NetworkManager module manages message process task, which provides interfaces to customize communication agreements and compression.
ParameterHandler is responsible for backend computation in Server; and Trainer is in charge of backend computation in Client.

Server#

The connection between NetworkManager and ParameterServerHandler in Server is shown as below. NetworkManager processes message and calls ParameterServerHandler.on_receive() method, while ParameterServerHandler performs training as well as computation process of server (model aggregation for example), and updates the global model.

Client#

Client shares similar design and structure with Server, with NetworkManager in charge of message processing as well as network communication with server, and Trainer for client local training procedure.

Communication#

FedLab furnishes both synchronous and asynchronous communication patterns, and their corresponding communication logics of NetworkManager is shown as below.

Synchronous FL: each round is launched by server, that is, server performs clients sampling first then broadcasts global model parameters.
Asynchronous FL [1]: each round is launched by clients, that is, clients request current global model parameters then perform local training.

Experimental Scene#

FedLab supports both single machine and multi-machine FL simulations, with standalone mode for single machine experiments, while cross-machine mode and hierarchical mode for multi-machine experiments.

Standalone#

FedLab implements SerialTrainer for FL simulation in single system process. SerialTrainer allows user to simulate a FL system with multiple clients executing one by one in serial in one SerialTrainer. It is designed for simulation in environment with limited computation resources.

Cross-process#

FedLab enables FL simulation tasks to be deployed on multiple processes with correct network configuration (these processes can be run on single or multiple machines). More flexibly in parallel, SerialTrainer can replace the regular Trainer directly. Users can balance the calculation burden among processes by choosing different Trainer. In practice, machines with more computation resources can be assigned with more workload of calculation.

Note

All machines must be in the same network (LAN or WAN) for cross-process deployment.

Hierarchical#

Hierarchical mode for FedLab is designed for situation tasks on multiple computer clusters (in different LAN) or the real-world scenes. To enable the inter-connection for different computer clusters, FedLab develops Scheduler as middle-server process to connect client groups. Each Scheduler manages the communication between the global server and clients in a client group. And server can communicate with clients in different LAN via corresponding Scheduler. The computation mode of a client group for each scheduler can be either standalone or cross-process.

A hierarchical FL system with K client groups is depicted as below.

Benchmarks#

FedLab also contains data partition settings [2], and implementations of FL algorithms [3]. For more information please see our FedLab-benchmarks repo. More benchmarks and FL algorithms demos are coming.

Installation & Set up#

FedLab can be installed by source code or pip.

Source Code#

Install lastest version from GitHub:

$ git clone git@github.com:SMILELab-FL/FedLab.git
$ cd FedLab

Install dependencies:

$ pip install -r requirements.txt

Pip#

Install stable version with pip:

$ pip install fedlab==$version$

Dataset Download#

FedLab provides common dataset used in FL researches.

Download procedure scripts are available in fedlab_benchmarks/datasets. For details of dataset, please follow README.md.

Tutorials#

FedLab standardizes FL simulation procedure, including synchronous algorithm, asynchronous algorithm [1] and communication compression [4]. FedLab provides modular tools and standard implementations to simplify FL research.

Distributed Communication#

Initialize distributed network#

FedLab uses torch.distributed as point-to-point communication tools. The communication backend is Gloo as default. FedLab processes send/receive data through TCP network connection. Here is the details of how to initialize the distributed network.

You need to assign right ethernet to DistNetwork, making sure torch.distributed network initialization works. DistNetwork is for quickly network configuration, which you can create one as follows:

from fedlab.core.network import DistNetwork
world_size = 10
rank = 0  # 0 for server, other rank for clients
ethernet = None
server_ip = '127.0.0.1'
server_port = 1234
network = DistNetwork(address=(server_ip, server_port), world_size, rank, ethernet)

network.init_network_connection() # call this method to start connection.
network.close_network_connection() # call this method to shutdown connection.

The (server_ip, server_port) is the address of server. please be aware of that the rank of server is 0 as default.
Make sure world_size is the same across process.
Rank should be different (from 0 to world_size-1).
world_size = 1 (server) + client number.
The ethernet is None as default. torch.distributed will try finding the right ethernet automatically.
The ethernet_name must be checked (using ifconfig). Otherwise, network initialization would fail.

If the automatically detected interface does not work, users are required to assign a right network interface for Gloo, by assigning in code or setting the environment variables GLOO_SOCKET_IFNAME, for example export GLOO_SOCKET_IFNAME=eth0 or os.environ['GLOO_SOCKET_IFNAME'] = "eth0".

Note

Check the available ethernet:

$ ifconfig

Point-to-point communication#

In recent update, we hide the communication details from user and provide simple APIs. DistNetwork now provies two basic communication APIs: send() and recv(). These APIs suppor flexible pytorch tensor communication.

Sender process:

network = DistNetwork(address=(server_ip, server_port), world_size, rank, ethernet)
network.init_network_connection()
network.send(content, message_code, dst)
network.close_network_connection()

Receiver process:

network = DistNetwork(address=(server_ip, server_port), world_size, rank, ethernet)
network.init_network_connection()
sender_rank, message_code, content = network.recv(src)
#################################
#                               #
#  local process with content.  #
#                               #
#################################
network.close_network_connection()

Note

Currently, following restrictions need to be noticed：

Tensor list: send() accepts a python list with tensors.
Data type: send() doesn’t accept tensors of different data type. In other words, FedLab force all appended tensors to be the same data type as the first appended tensor. Torch data types like [torch.int8, torch.int16, torch.int32, torch.int64, torch.float16, torch.float32, torch.float64] are supported.

Further understanding of FedLab communication#

FedLab pack content into a pre-defined package data structure. send() and recv() are implemented like:

def send(self, content=None, message_code=None, dst=0):
    """Send tensor to process rank=dst"""
    pack = Package(message_code=message_code, content=content)
    PackageProcessor.send_package(pack, dst=dst)

def recv(self, src=None):
    """Receive tensor from process rank=src"""
    sender_rank, message_code, content = PackageProcessor.recv_package(
        src=src)
    return sender_rank, message_code, content

Create package#

The basic communication unit in FedLab is called package. The communication module of FedLab is in fedlab/core/communicator. Package defines the basic data structure of network package. It contains header and content.

p = Package()
p.header   # A tensor with size = (5,).
p.content  # A tensor with size = (x,).

Currently, you can create a network package from following methods:

initialize with tensor

tensor = torch.Tensor(size=(10,))
package = Package(content=tensor)

initialize with tensor list

tensor_sizes = [10, 5, 8]
tensor_list = [torch.rand(size) for size in tensor_sizes]
package = Package(content=tensor_list)

append a tensor to exist package

tensor = torch.Tensor(size=(10,))
package = Package(content=tensor)

new_tensor = torch.Tensor(size=(8,))
package.append_tensor(new_tensor)

append a tensor list to exist package

tensor_sizes = [10, 5, 8]
tensor_list = [torch.rand(size) for size in tensor_sizes]

package = Package()
package.append_tensor_list(tensor_list)

Two static methods are provided by Package to parse header and content:

p = Package()
Package.parse_header(p.header)  # necessary information to describe the package
Package.parse_content(p.slices, p.content) # tensor list associated with the tensor sequence appended into.

Send package#

The point-to-point communicating agreements is implemented in PackageProcessor module. PackageProcessor is a static class to manage package sending/receiving procedure.

User can send a package to a process with rank=0 (the parameter dst must be assigned):

p = Package()
PackageProcessor.send_package(package=p, dst=0)

or, receive a package from rank=0 (set the parameter src=None to receive package from any other process):

sender_rank, message_code, content = PackageProcessor.recv_package(src=0)

Communication Strategy#

Communication strategy is implemented by (ClientManager,ServerManager) pair collaboratively.

The prototype of NetworkManager is defined in fedlab.core.network_manager, which is also a subclass of torch.multiprocessing.process.

Typically, standard implementations is shown in fedlab.core.client.manager and fedlab.core.server.manager. NetworkManager manages network operation and control flow procedure.

Base class definition shows below:

class NetworkManager(Process):
    """Abstract class

    Args:
        newtork (DistNetwork): object to manage torch.distributed network communication.
    """

    def __init__(self, network):
        super(NetworkManager, self).__init__()
        self._network = network

    def run(self):
        """
        Main Process:
            1. Initialization stage.

            2. FL communication stage.

            3. Shutdown stage, then close network connection.
        """
        self.setup()
        self.main_loop()
        self.shutdown()

    def setup(self, *args, **kwargs):
        """Initialize network connection and necessary setups.

        Note:
            At first, ``self._network.init_network_connection()`` is required to be called.
            Overwrite this method to implement system setup message communication procedure.
        """
        self._network.init_network_connection()

    def main_loop(self, *args, **kwargs):
        """Define the actions of communication stage."""
        raise NotImplementedError()

    def shutdown(self, *args, **kwargs):
        """Shut down stage"""
        self._network.close_network_connection()

FedLab provides 2 standard communication pattern implementations: synchronous and asynchronous. And we encourage users create new FL communication pattern for their own algorithms.

You can customize process flow by: 1. create a new class inherited from corresponding class in our standard implementations; 2. overwrite the functions in target stage. To sum up, communication strategy can be customized by overwriting as the note below mentioned.

Note

setup() defines the network initialization stage. Can be used for FL algorithm initialization.
main_loop() is the main process of client and server. User need to define the communication strategy for both client and server manager.
shutdown() defines the shutdown stage.

Importantly, ServerManager and ClientManager should be defined and used as a pair. The control flow and information agreements should be compatible. FedLab provides standard implementation for typical synchronous and asynchronous, as depicted below.

Synchronous mode#

Synchronous communication involves SynchronousServerManager and PassiveClientManager. Communication procedure is shown as follows.

Asynchronous mode#

Asynchronous is given by ServerAsynchronousManager and ClientActiveManager. Communication procedure is shown as follows.

Customization#

Initialization stage#

Initialization stage is represented by manager.setup() function.

User can customize initialization procedure as follows(use ClientManager as example):

from fedlab.core.client.manager import PassiveClientManager

class CustomizeClientManager(PassiveClientManager):

    def __init__(self, trainer, network):
        super().__init__(trainer, network)

    def setup(self):
        super().setup()
        *****************************
        *                           *
        *      Write Code Here      *
        *                           *
        *****************************

Communication stage#

After Initialization Stage, user can define main_loop() to define main process for server and client. To standardize FedLab’s implementation, here we give the main_loop() of PassiveClientManager: and SynchronousServerManager for example.

Client part:

def main_loop(self):
    """Actions to perform when receiving new message, including local training

    Main procedure of each client:
        1. client waits for data from server （PASSIVELY）
        2. after receiving data, client trains local model.
        3. client synchronizes with server actively.
    """
    while True:
        sender_rank, message_code, payload = self._network.recv(src=0)
        if message_code == MessageCode.Exit:
            break
        elif message_code == MessageCode.ParameterUpdate:
            self._trainer.local_process(payload=payload)
            self.synchronize()
        else:
            raise ValueError("Invalid MessageCode {}.".format(message_code))

Server Part:

def main_loop(self):
    """Actions to perform in server when receiving a package from one client.

    Server transmits received package to backend computation handler for aggregation or others
    manipulations.

    Loop:
        1 activate clients.

        2 listen for message from clients -> transmit received parameters to server backend.

    Note:
        Communication agreements related: user can overwrite this function to customize
        communication agreements. This method is key component connecting behaviors of
        :class:`ParameterServerBackendHandler` and :class:`NetworkManager`.

    Raises:
        Exception: Unexpected :class:`MessageCode`.
    """
    while self._handler.stop_condition() is not True:
        activate = threading.Thread(target=self.activate_clients)
        activate.start()
        while True:
            sender_rank, message_code, payload = self._network.recv()
            if message_code == MessageCode.ParameterUpdate:
                if self._handler.iterate_global_model(sender_rank, payload=paylaod):
                    break
            else:
                raise Exception(
                    raise ValueError("Invalid MessageCode {}.".format(message_code))

Shutdown stage#

shutdown() will be called when main_loop() finished. You can define the actions for client and server seperately.

Typically in our implementation, shutdown stage is started by server. It will send a message with MessageCode.Exit to inform client to stop its main loop.

Codes below is the actions of SynchronousServerManager in shutdown stage.

def shutdown(self):
    self.shutdown_clients()
    super().shutdown()

def shutdown_clients(self):
    """Shut down all clients.

    Send package to every client with :attr:`MessageCode.Exit` to client.
    """
    for rank in range(1, self._network.world_size):
        print("stopping clients rank:", rank)
        self._network.send(message_code=MessageCode.Exit, dst=rank)

Federated Optimization#

Standard FL Optimization contains two parts: 1. local train in client; 2. global aggregation in server. Local train and aggregation procedure are customizable in FedLab. You need to define ClientTrainer and ServerHandler.

Since ClientTrainer and ServerHandler are required to manipulate PyTorch Model. They are both inherited from ModelMaintainer.

class ModelMaintainer(object):
    """Maintain PyTorch model.

    Provide necessary attributes and operation methods. More features with local or global model
    will be implemented here.

    Args:
        model (torch.nn.Module): PyTorch model.
        cuda (bool): Use GPUs or not.
        device (str, optional): Assign model/data to the given GPUs. E.g., 'device:0' or 'device:0,1'. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
    """
    def __init__(self,
                model: torch.nn.Module,
                cuda: bool,
                device: str = None) -> None:
        self.cuda = cuda

        if cuda:
            # dynamic gpu acquire.
            if device is None:
                self.device = get_best_gpu()
            else:
                self.device = device
            self._model = deepcopy(model).cuda(self.device)
        else:
            self._model = deepcopy(model).cpu()

    def set_model(self, parameters: torch.Tensor):
        """Assign parameters to self._model."""
        SerializationTool.deserialize_model(self._model, parameters)

    @property
    def model(self) -> torch.nn.Module:
        """Return :class:`torch.nn.module`."""
        return self._model

    @property
    def model_parameters(self) -> torch.Tensor:
        """Return serialized model parameters."""
        return SerializationTool.serialize_model(self._model)

    @property
    def model_gradients(self) -> torch.Tensor:
        """Return serialized model gradients."""
        return SerializationTool.serialize_model_gradients(self._model)

    @property
    def shape_list(self) -> List[torch.Tensor]:
        """Return shape of model parameters.

        Currently, this attributes used in tensor compression.
        """
        shape_list = [param.shape for param in self._model.parameters()]
        return shape_list

Client local training#

The basic class of ClientTrainer is shown below, we encourage users define local training process following our code pattern:

class ClientTrainer(ModelMaintainer):
    """An abstract class representing a client trainer.

    In FedLab, we define the backend of client trainer show manage its local model.
    It should have a function to update its model called :meth:`local_process`.

    If you use our framework to define the activities of client, please make sure that your self-defined class
    should subclass it. All subclasses should overwrite :meth:`local_process` and property ``uplink_package``.

    Args:
        model (torch.nn.Module): PyTorch model.
        cuda (bool): Use GPUs or not.
        device (str, optional): Assign model/data to the given GPUs. E.g., 'device:0' or 'device:0,1'. Defaults to ``None``.
    """

    def __init__(self,
                model: torch.nn.Module,
                cuda: bool,
                device: str = None) -> None:
        super().__init__(model, cuda, device)

        self.client_num = 1  # default is 1.
        self.dataset = FedDataset() # or Dataset
        self.type = ORDINARY_TRAINER

    def setup_dataset(self):
        """Set up local dataset ``self.dataset`` for clients."""
        raise NotImplementedError()

    def setup_optim(self):
        """Set up variables for optimization algorithms."""
        raise NotImplementedError()

    @property
    @abstractmethod
    def uplink_package(self) -> List[torch.Tensor]:
        """Return a tensor list for uploading to server.

            This attribute will be called by client manager.
            Customize it for new algorithms.
        """
        raise NotImplementedError()

    @abstractclassmethod
    def local_process(self, payload: List[torch.Tensor]):
        """Manager of the upper layer will call this function with accepted payload

            In synchronous mode, return True to end current FL round.
        """
        raise NotImplementedError()

    def train(self):
        """Override this method to define the training procedure. This function should manipulate :attr:`self._model`."""
        raise NotImplementedError()

    def validate(self):
        """Validate quality of local model."""
        raise NotImplementedError()

    def evaluate(self):
        """Evaluate quality of local model."""
        raise NotImplementedError()

Overwrite ClientTrainer.local_process() to define local procedure. Typically, you need to implement standard training pipeline of PyTorch.
Attributes model and model_parameters is is associated with self._model. Please make sure the function local_process() will manipulate self._model.

A standard implementation of this part is in :class:`SGDClientTrainer`.

Server global aggregation#

Calculation tasks related with PyTorch should be define in ServerHandler part. In FedLab, our basic class of Handler is defined in ServerHandler.

class ServerHandler(ModelMaintainer):
    """An abstract class representing handler of parameter server.

    Please make sure that your self-defined server handler class subclasses this class

    Example:
        Read source code of :class:`SyncServerHandler` and :class:`AsyncServerHandler`.

    Args:
        model (torch.nn.Module): PyTorch model.
        cuda (bool): Use GPUs or not.
        device (str, optional): Assign model/data to the given GPUs. E.g., 'device:0' or 'device:0,1'. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
    """
    def __init__(self,
                model: torch.nn.Module,
                cuda: bool,
                device: str = None) -> None:
        super().__init__(model, cuda, device)

    @property
    @abstractmethod
    def downlink_package(self) -> List[torch.Tensor]:
        """Property for manager layer. Server manager will call this property when activates clients."""
        raise NotImplementedError()

    @property
    @abstractmethod
    def if_stop(self) -> bool:
        """:class:`NetworkManager` keeps monitoring this attribute, and it will stop all related processes and threads when ``True`` returned."""
        return False

    @abstractmethod
    def setup_optim(self):
        """Override this function to load your optimization hyperparameters."""
        raise NotImplementedError()

    @abstractmethod
    def global_update(self, buffer):
        raise NotImplementedError()

    @abstractmethod
    def load(self, payload):
        """Override this function to define how to update global model (aggregation or optimization)."""
        raise NotImplementedError()

    @abstractmethod
    def evaluate(self):
        """Override this function to define the evaluation of global model."""
        raise NotImplementedError()

User can define server aggregation strategy by finish following functions:

You can overwrite _update_global_model() to customize global procedure.
_update_global_model() is required to manipulate global model parameters (self._model).
Summarised FL aggregation strategies are implemented in fedlab.utils.aggregator.

A standard implementation of this part is in SyncParameterServerHandler.

Federated Dataset and DataPartitioner#

Sophisticated in real world, FL need to handle various kind of data distribution scenarios, including iid and non-iid scenarios. Though there already exists some datasets and partition schemes for published data benchmark, it still can be very messy and hard for researchers to partition datasets according to their specific research problems, and maintain partition results during simulation. FedLab provides fedlab.utils.dataset.partition.DataPartitioner that allows you to use pre-partitioned datasets as well as your own data. DataPartitioner stores sample indices for each client given a data partition scheme. Also, FedLab provides some extra datasets that are used in current FL researches while not provided by official Pytorch torchvision.datasets yet.

Note

Current implementation and design of this part are based on LEAF [2], Acar et al. [5], Yurochkin et al. [6] and NIID-Bench [7].

Vision Data#

CIFAR10#

FedLab provides a number of pre-defined partition schemes for some datasets (such as CIFAR10) that subclass fedlab.utils.dataset.partition.DataPartitioner and implement functions specific to particular partition scheme. They can be used to prototype and benchmark your FL algorithms.

Tutorial for CIFAR10Partitioner: CIFAR10 tutorial.

CIFAR100#

Notebook tutorial for CIFAR100Partitioner: CIFAR100 tutorial.

FMNIST#

Notebook tutorial for data partition of FMNIST (FashionMNIST) : FMNIST tutorial.

MNIST#

MNIST is very similar with FMNIST, please check FMNIST tutorial.

SVHN#

Data partition tutorial for SVHN: SVHN tutorial

CelebA#

Data partition for CelebA: CelebA tutorial.

FEMNIST#

Data partition of FEMNIST: FEMNIST tutorial.

Text Data#

Shakespeare#

Data partition of Shakespeare dataset: Shakespeare tutorial.

Sent140#

Data partition of Sent140: Sent140 tutorial.

Reddit#

Data partition of Reddit: Reddit tutorial.

Tabular Data#

Adult#

Adult is from LIBSVM Data. Its original source is from UCI/Adult. FedLab provides both Dataset and DataPartitioner for Adult. Notebook tutorial for Adult: Adult tutorial.

Covtype#

Covtype is from LIBSVM Data. Its original source is from UCI/Covtype. FedLab provides both Dataset and DataPartitioner for Covtype. Notebook tutorial for Covtype: Covtype tutorial.

RCV1#

RCV1 is from LIBSVM Data. Its original source is from UCI/RCV1. FedLab provides both Dataset and DataPartitioner for RCV1. Notebook tutorial for RCV1: RCV1 tutorial.

Synthetic Data#

FCUBE#

FCUBE is a synthetic dataset for federated learning. FedLab provides both Dataset and DataPartitioner for FCUBE. Tutorial for FCUBE: FCUBE tutorial.

LEAF-Synthetic#

LEAF-Synthetic is a federated dataset proposed by LEAF. Client number, class number and feature dimensions can all be customized by user.

Please check LEAF-Synthetic for more details.

Deploy FedLab Process in a Docker Container#

Why docker?#

The communication APIs of FedLab is built on torch.distributed. In cross-process scene, when multiple FedLab processes are deployed on the same machine, GPU memory buckets will be created automatically however which are not used in our framework. We can start the FedLab processes in different docker containers to avoid triggering GPU memory buckets (to save GPU memory).

Setup docker environment#

In this section, we introduce how to setup a docker image for FedLab program. Here we provide the Dockerfile for building a FedLab image. Our FedLab environment is based on PytTorch. Therefore, we just need install FedLab on the provided PytTorch image.

Dockerfile:

# This is an example of fedlab installation via Dockerfile

# replace the value of TORCH_CONTAINER with pytorch image that satisfies your cuda version
# you can find it in https://hub.docker.com/r/pytorch/pytorch/tags
ARG TORCH_CONTAINER=1.5-cuda10.1-cudnn7-runtime

FROM pytorch/pytorch:${TORCH_CONTAINER}

RUN pip install --upgrade pip \
    & pip uninstall -y torch torchvision  \
    & conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ \
    & conda config --set show_channel_urls yes \
    & mkdir /root/tmp/

# replace with the correct install command, which you can find in https://pytorch.org/get-started/previous-versions/
RUN conda install -y pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch

# pip install fedlab
RUN TMPDIR=/root/tmp/ pip install -i https://pypi.mirrors.ustc.edu.cn/simple/ fedlab

Dockerfile for different platforms#

The steps of modifying Dockerfile for different platforms:

Step 1: Find an appropriate base pytorch image for your platform from dockerhub https://hub.docker.com/r/pytorch/pytorch/tags. Then, replace the value of TORCH_CONTAINER in demo dockerfile.
Step 2: To install specific PyTorch version, you need to choose a correct install command, which can be find in https://pytorch.org/get-started/previous-versions/. Then, modify the 16-th command in demo dockerfile.
Step 3: Build the images for your own platform by running the command below in the dir of Dockerfile.
```
$ docker build -t image_name .
```

Warning

Using “–gpus all” and “–network=host” when start a docker container:

$ docker run -itd --gpus all --network=host b23a9c46cd04(image name) /bin/bash

If you are not in China area, it is ok to remove line 11,12 and “-i https://pypi.mirrors.ustc.edu.cn/simple/” in line 19.

Finally: Run your FedLab process in the different started containers.

Learn Distributed Network Basics

Step-by-step guide on distributed network setup and package transmission.

How to Customize Communication Strategy?

Use NetworkManager to customize communication strategies, including synchronous and asynchronous communication.

How to Customize Federated Optimization?

Define your own model optimization process for both server and client.

Federated Datasets and Data Partitioner

Get federated datasets and data partition for IID and non-IID setting.

Examples#

Quick Start#

In this page, we introduce the provided quick start demos. And the start scripts for FL simulation system with FedLab in different scenario. We implement FedAvg algorithm with MLP network and partitioned MNIST dataset across clients.

Source code can be seen in fedlab/examples/.

Download dataset#

FedLab provides scripts for common dataset download and partition process. Besides, FL dataset baseline LEAF [2] is also implemented and compatible with PyTorch interfaces.

Codes related to dataset download process are available at fedlab_benchamrks/datasets/{dataset name}.

Download MNIST/CIFAR10

$ cd fedlab_benchamrks/datasets/{mnist or cifar10}/
$ python download_{dataset}.py

Partition

Run follow python file to generate partition file.

$ python {dataset}_partition.py

Source codes of partition scripts:

import torchvision
from fedlab.utils.functional import save_dict
from fedlab.utils.dataset.slicing import noniid_slicing, random_slicing

trainset = torchvision.datasets.CIFAR10(root=root, train=True, download=True)
# trainset = torchvision.datasets.MNIST(root=root, train=True, download=True)

data_indices = noniid_slicing(trainset, num_clients=100, num_shards=200)
save_dict(data_indices, "cifar10_noniid.pkl")

data_indices = random_slicing(trainset, num_clients=100)
save_dict(data_indices, "cifar10_iid.pkl")

data_indices is a dict mapping from client id to data indices(list) of raw dataset. FedLab provides random partition and non-I.I.D. partition methods, in which the noniid partition method is totally re-implementation in paper FedAvg.

LEAF dataset process

Please follow the FedLab benchmark to learn how to generate LEAF related dataset partition.

Run FedLab demos#

FedLab provides both asynchronous and synchronous standard implementation demos for uses to learn. We only introduce the usage of synchronous FL system simulation demo(FedAvg) with different scenario in this page. (Code structures are similar.)

We are very confident in the readability of FedLab code, so we recommend that users read the source code according to the following demos for better understanding.

1. Standalone#

Source code is under fedlab/examples/standalone-mnist. This is a standard usage of SerialTrainer which allows users to simulate a group of clients with a single process.

$ python standalone.py --total_client 100 --com_round 3 --sample_ratio 0.1 --batch_size 100 --epochs 5 --lr 0.02

$ bash launch_eg.sh

Run command above to start a single process simulating FedAvg algorithm with 100 clients with 10 communication round in total, with 10 clients sampled randomly at each round .

2. Cross-process#

Source code is under fedlab/examples/cross-process-mnist

Start a FL simulation with 1 server and 2 clients.

$ bash launch_eg.sh

The content of launch_eg.sh is:

python server.py --ip 127.0.0.1 --port 3001 --world_size 3 --round 3 &

python client.py --ip 127.0.0.1 --port 3001 --world_size 3 --rank 1 &

python client.py --ip 127.0.0.1 --port 3001 --world_size 3 --rank 2  &

wait

Cross-process scenario allows users deploy their FL system in computer cluster. Although in this case, we set the address of server as localhost. Then three process will communicate with each other following standard FL procedure.

Note

Due to the rank of torch.distributed is unique for every process. Therefore, we use rank represent client id in this scenario.

3. Cross-process with SerialTrainer#

SerialTrainer uses less computer resources (single process) to simulate multiple clients. Cross-pross is suit for computer cluster deployment, simulating data-center FL system. In our experiment, the world size of torch.distributed can’t more than 50 (Denpends on clusters), otherwise, the socket will crash, which limited the client number of FL simulation.

To improve scalability, FedLab provides scale standard implementation to combine SerialTrainer and ClientManager, which allows a single process simulate multiple clients.

Source codes are available in fedlab_benchamrks/algorithm/fedavg/scale/{experiment setting name}.

Here, we take mnist-cnn as example to introduce this demo. In this demo, we set world_size=11 (1 ServerManager, 10 ClientManagers), and each ClientManager represents 10 local client dataset partition. Our data partition strategy follows the experimental setting of fedavg as well. In this way, we only use 11 processes to simulate a FL system with 100 clients.

To start this system, you need to open at least 2 terminal (we still use localhost as demo. Use multiple machines is OK as long as with right network configuration):

server (terminal 1)

$ python server.py --ip 127.0.0.1 --port 3002 --world_size 11

clients (terminal 2)

$ bash start_clt.sh 11 1 10 # launch clients from rank 1 to rank 10 with world_size 11

The content of start_clt.sh:

for ((i=$2; i<=$3; i++))
do
{
    echo "client ${i} started"
    python client.py --world_size $1 --rank ${i} &
    sleep 2s # wait for gpu resources allocation
}
done
wait

4. Hierachical#

The demo of Hierachical with hybrid client (standalone and serial trainer) is given in fedlab/examples/hierarchical-hybrid-mnist.

Run all scripts together:

$ bash launch_eg.sh

Run scripts seperately:

# Top server in terminal 1
$ bash launch_topserver_eg.sh

# Scheduler1 + Ordinary trainer with 1 client + Serial trainer with 10 clients in terminal 2:
bash launch_cgroup1_eg.sh

# Scheduler2 + Ordinary trainer with 1 client + Serial trainer with 10 clients in terminal 3:
$ bash launch_cgroup2_eg.sh

PyTorch version of LEAF#

FedLab migrates the TensorFlow version of LEAF dataset to the PyTorch framework, and provides the implementation of dataloader for the corresponding dataset. The unified interface is in ``fedlab_benchmarks/leaf/dataloader.py``

This markdown file introduces the process of using LEAF dataset in FedLab.

Description of Leaf datasets#

The LEAF benchmark contains the federation settings of Celeba, femnist, Reddit, sent140, shakespeare and synthetic datasets. With reference to leaf-readme.md , the introduction the total number of users and the corresponding task categories of leaf datasets are given below.

FEMNIST

Overview: Image Dataset.
Details: 62 different classes (10 digits, 26 lowercase, 26 uppercase), images are 28 by 28 pixels (with option to make them all 128 by 128 pixels), 3500 users.
Task: Image Classification.

Sentiment140

Overview: Text Dataset of Tweets.
Details 660120 users.
Task: Sentiment Analysis.

Shakespeare

Overview: Text Dataset of Shakespeare Dialogues.
Details: 1129 users (reduced to 660 with our choice of sequence length. See bug.)
Task: Next-Character Prediction.

Celeba

Overview: Image Dataset based on the Large-scale CelebFaces Attributes Dataset.
Details: 9343 users (we exclude celebrities with less than 5 images).
Task: Image Classification (Smiling vs. Not smiling).

Synthetic Dataset

Overview: We propose a process to generate synthetic, challenging federated datasets. The high-level goal is to create devices whose true models are device-dependant. To see a description of the whole generative process, please refer to the paper.
Details: The user can customize the number of devices, the number of classes and the number of dimensions, among others.
Task: Classification.

Overview: We preprocess the Reddit data released by pushshift.io corresponding to December 2017.
Details: 1,660,820 users with a total of 56,587,343 comments.
Task: Next-word Prediction.

Download and preprocess data#

For the six types of leaf datasets, refer to leaf/data and provide data download and preprocessing scripts in fedlab _ benchmarks/datasets/data. In order to facilitate developers to use leaf, fedlab integrates the download and processing scripts of leaf six types of data sets into fedlab_benchmarks/datasets/data, which stores the download scripts of various data sets.

Common structure of leaf dataset folders:

/FedLab/fedlab_benchmarks/datasets/{leaf_dataset_name}

   ├── {other_useful_preprocess_util}
   ├── prerpocess.sh
   ├── stats.sh
   └── README.md

preprocess.sh: downloads and preprocesses the dataset
stats.sh: performs information statistics on all data (stored in ./data/all_data/all_data.json) processed by preprocess.sh
README.md: gives a detailed description of the process of downloading and preprocessing the dataset, including parameter descriptions and precautions.

Developers can directly run the executable script ``create_datasets_and_save.sh`` to obtain the dataset, process and store the corresponding dataset data in the form of a pickle file. This script provides an example of using the preprocess.sh script, and developers can modify the parameters according to application requirements.

preprocess.sh Script usage example:

cd fedlab_benchmarks/datasets/data/femnist
bash preprocess.sh -s niid --sf 0.05 -k 0 -t sample

cd fedlab_benchmarks/datasets/data/shakespeare
bash preprocess.sh -s niid --sf 0.2 -k 0 -t sample -tf 0.8

cd fedlab_benchmarks/datasets/data/sent140
bash ./preprocess.sh -s niid --sf 0.05 -k 3 -t sample

cd fedlab_benchmarks/datasets/data/celeba
bash ./preprocess.sh -s niid --sf 0.05 -k 5 -t sample

cd fedlab_benchmarks/datasets/data/synthetic
bash ./preprocess.sh -s niid --sf 1.0 -k 5 -t sample --tf 0.6

# for reddit, see its README.md to download preprocessed dataset manually

By setting parameters for preprocess.sh, the original data can be sampled and spilted. The readme.md in each dataset folder provides the example and explanation of script parameters, the common parameters are:

-s := ‘iid’ to sample in an i.i.d. manner, or ‘niid’ to sample in a non-i.i.d. manner; more information on i.i.d. versus non-i.i.d. is included in the ‘Notes’ section.
--sf := fraction of data to sample, written as a decimal; default is 0.1.
-k := minimum number of samples per user
-t := ‘user’ to partition users into train-test groups, or ‘sample’ to partition each user’s samples into train-test groups
--tf := fraction of data in training set, written as a decimal; default is 0.9, representing train set: test set = 9:1.

At present, FedLab’s Leaf experiment need provided training data and test data, so we needs to provide related data training set-test set splitting parameter for preprocess.sh to carry out the experiment, default is 0.9.

If you need to obtain or split data again, make sure to delete data folder in the dataset directory before re-running preprocess.sh to download and preprocess data.

Pickle file stores Dataset.#

In order to speed up developers’ reading data, fedlab provides a method of processing raw data into Dataset and storing it as a pickle file. The Dataset of the corresponding data of each client can be obtained by reading the pickle file after data processing.

set the parameters and run create_pickle_dataset.py. The usage example is as follows:

cd fedlab_benchmarks/leaf/process_data
python create_pickle_dataset.py --data_root "../../datasets" --save_root "./pickle_dataset" --dataset_name "shakespeare"

Parameter Description:

data_root : the root path for storing leaf data sets, which contains all leaf data sets; If you use the Fedlab_benchmarks/datasets/ provided by fedlab to download leaf data, ‘data_root’ can be set to this path, a relative address of which is shown in this example.
save_root: directory to store the pickle file address of the processed Dataset; Each dataset Dataset will be saved in {save_root}/{dataset_name}/{train,test}; the example is to create a pickle_dataset folder under the current path to store all pickle dataset files.
dataset_name: Specify the name of the leaf data set to be processed. There are six options {femnist, shakespeare, celeba, sent140, synthetic, reddit}.

Dataloader loading data set#

Leaf datasets are loaded by dataloader.py (located under fedlab_benchmarks/leaf/dataloader.py). All returned data types are pytorch Dataloader.

By calling this interface and specifying the name of the data set, the corresponding Dataloader can be obtained.

Example of use:

from leaf.dataloader import get_LEAF_dataloader
def get_femnist_shakespeare_dataset(args):
    if args.dataset == 'femnist' or args.dataset == 'shakespeare':
        trainloader, testloader = get_LEAF_dataloader(dataset=args.dataset,
                                                      client_id=args.rank)
    else:
        raise ValueError("Invalid dataset:", args.dataset)

    return trainloader, testloader

Run experiment#

The current experiment of LEAF data set is the single-machine multi-process scenario under FedAvg’s Cross machine implement, and the tests of femnist and Shakespeare data sets have been completed.

Run `fedlab_benchmarks/fedavg/cross_machine/LEAF_test.sh` to quickly execute the simulation experiment of fedavg under leaf data set.

Quick Start

PyTorch version of LEAF

Contributing to FedLab#

Reporting bugs#

We use GitHub issues to track all bugs and feature requests. Feel free to open an issue if you have found a bug or wish to see a feature implemented.

In case you experience issues using this package, do not hesitate to submit a ticket to the Bug Tracker. You are also welcome to post feature requests or pull requests.

Contributing Code#

You’re welcome to contribute to this project through Pull Request. By contributing, you agree that your contributions will be licensed under Apache License, Version 2.0

We encourage you to contribute to the improvement of FedLab or the FedLab implementation of existing FL methods. The preferred workflow for contributing to FedLab is to fork the main repository on GitHub, clone, and develop on a branch. Steps as follow:

Fork the project repository by clicking on the ‘Fork’. For contributing new features, please fork FedLab core repo or new implementations for FedLab benchmarks repo.

Clone your fork of repo from your GitHub to your local:

$ git clone git@github.com:YourLogin/FedLab.git
$ cd FedLab

Create a new branch to save your changes:
$ git checkout -b my-feature
Develop the feature on your branch.
$ git add modified_files $ git commit

Pull Request Checklist#

Please follow the file structure below for new features or create new file if there are something new.

fedlab
    ├── core
    │   ├── communicator            # communication module
    │   ├── client                  # client related implementations
    │   └── server                  # server related implementations
    │       └── hierarchical        # hierarchical communication pattern modules
    └── utils                       # functional modules
        └── dataset                 # functional modules about dataset

The code should provide test cases using unittest.TestCase. And ensure all local tests passed:
$ python test_bench.py
All public methods should have informative docstrings with sample usage presented as doctests when appropriate. Docstring and code should follow Google Python Style Guide: 中文版 | English.

Reference#

[1]

Cong Xie, Sanmi Koyejo, and Indranil Gupta. Asynchronous federated optimization. arXiv preprint arXiv:1903.03934, 2019.

[2]

Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečn\`y, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. Leaf: a benchmark for federated settings. arXiv preprint arXiv:1812.01097, 2018.

[3]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, 1273–1282. PMLR, 2017.

[4]

Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J Dally. Deep gradient compression: reducing the communication bandwidth for distributed training. arXiv preprint arXiv:1712.01887, 2017.

[5]

Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama. Federated learning based on dynamic regularization. In International Conference on Learning Representations. 2020.

[6]

Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni. Bayesian nonparametric federated learning of neural networks. In International Conference on Machine Learning, 7252–7261. PMLR, 2019.

[7]

Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He. Federated learning on non-iid data silos: an experimental study. arXiv preprint arXiv:2102.02079, 2021.

[8]

Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni. Federated learning with matched averaging. arXiv preprint arXiv:2002.06440, 2020.

API Reference#

This page contains auto-generated API reference documentation [1].

fedlab#

contrib#

algorithm#

basic_client#

Module Contents#

`SGDClientTrainer`	Client backend handler, this class provides data process method to upper layer.
`SGDSerialClientTrainer`	Train multiple clients in a single process.

class SGDClientTrainer(model: torch.nn.Module, cuda: bool = False, device: str = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.core.client.trainer.ClientTrainer

Client backend handler, this class provides data process method to upper layer.

Parameters:

model (torch.nn.Module) – PyTorch model.
cuda (bool, optional) – use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – :object of Logger.

property uplink_package#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

setup_dataset(dataset)#: Set up local dataset self.dataset for clients.

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id)#

Manager of the upper layer will call this function with accepted payload

In synchronous mode, return True to end current FL round.

train(model_parameters, train_loader) → None#

Client trains its local model on local dataset.

Parameters:: model_parameters (torch.Tensor) – Serialized model parameters.

class SGDSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.core.client.trainer.SerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

property uplink_package#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

setup_dataset(dataset)#: Override this function to set up local dataset for clients

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(model_parameters, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

basic_server#

Module Contents#

`SyncServerHandler`	Synchronous Parameter Server Handler.
`AsyncServerHandler`	Asynchronous Parameter Server Handler

class SyncServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.core.server.handler.ServerHandler

Synchronous Parameter Server Handler.

Backend of synchronous parameter server: this class is responsible for backend computing in synchronous server.

Synchronous parameter server will wait for every client to finish local training process before the next FL round.

Details in paper: http://proceedings.mlr.press/v54/mcmahan17a.html

Parameters:

model (torch.nn.Module) – model trained by federated learning.
global_round (int) – stop condition. Shut down FL system when global round is reached.
num_clients (int) – number of clients in FL. Default: 0 (initialized external).
sample_ratio (float) – the result of sample_ratio * num_clients is the number of clients for every FL round.
cuda (bool) – use GPUs or not. Default: False.
device (str, optional) – assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
sampler (FedSampler, optional) – assign a sampler to define the client sampling strategy. Default: random sampling with FedSampler.
logger (Logger, optional) – object of Logger.

property downlink_package: List[torch.Tensor]#: Property for manager layer. Server manager will call this property when activates clients.

property num_clients_per_round#

property if_stop#: NetworkManager keeps monitoring this attribute, and it will stop all related processes and threads when True returned.

sample_clients(num_to_sample=None)#: Return a list of client rank indices selected randomly. The client ID is from 0 to self.num_clients -1.

global_update(buffer)#

load(payload: List[torch.Tensor]) → bool#

Update global model with collected parameters from clients.

Note

Server handler will call this method when its client_buffer_cache is full. User can overwrite the strategy of aggregation to apply on model_parameters_list, and use SerializationTool.deserialize_model() to load serialized parameters after aggregation into self._model.

Parameters:: payload (list[torch.Tensor]) – A list of tensors passed by manager layer.

class AsyncServerHandler(model: torch.nn.Module, global_round: int, num_clients: int, cuda: bool = False, device: str = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.core.server.handler.ServerHandler

Asynchronous Parameter Server Handler

Update global model immediately after receiving a ParameterUpdate message Paper: https://arxiv.org/abs/1903.03934

Parameters:

model (torch.nn.Module) – Global model in server
global_round (int) – stop condition. Shut down FL system when global round is reached.
num_clients (int) – number of clients in FL.
cuda (bool) – Use GPUs or not.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
logger (Logger, optional) – Object of Logger.

property if_stop#: NetworkManager keeps monitoring this attribute, and it will stop all related processes and threads when True returned.

property downlink_package#: Property for manager layer. Server manager will call this property when activates clients.

setup_optim(alpha, strategy='constant', a=10, b=4)#

Setup optimization configuration.

Parameters:

alpha (float) – Weight used in async aggregation.
strategy (str, optional) – Adaptive strategy. constant, hinge and polynomial is optional. Default: constant.. Defaults to ‘constant’.
a (int, optional) – Parameter used in async aggregation.. Defaults to 10.
b (int, optional) – Parameter used in async aggregation.. Defaults to 4.

global_update(buffer)#

load(payload: List[torch.Tensor]) → bool#: Override this function to define how to update global model (aggregation or optimization).

adapt_alpha(receive_model_time)#: update the alpha according to staleness

cfl#

ditto#

Module Contents#

`DittoServerHandler`	Ditto server acts the same as fedavg server.
`DittoSerialClientTrainer`	Train multiple clients in a single process.

class DittoServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

Ditto server acts the same as fedavg server.

class DittoSerialClientTrainer(model, num, cuda=False, device=None, logger=None, personal=True)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

property uplink_package#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

setup_dataset(dataset)#: Override this function to set up local dataset for clients

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(global_model_parameters, local_model_parameters, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

fedavg#

Module Contents#

`FedAvgServerHandler`	FedAvg server handler.
`FedAvgClientTrainer`	Federated client with local SGD solver.
`FedAvgSerialClientTrainer`	Federated client with local SGD solver.

class FedAvgServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedAvg server handler.

global_update(buffer)#

class FedAvgClientTrainer(model: torch.nn.Module, cuda: bool = False, device: str = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_client.SGDClientTrainer

Federated client with local SGD solver.

global_update(buffer)#

class FedAvgSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Federated client with local SGD solver.

train(model_parameters, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

fedavgm#

Module Contents#

FedAvgMServerHandler

Hsu, Tzu-Ming Harry, Hang Qi, and Matthew Brown. "Measuring the effects of non-identical data distribution for federated visual classification." arXiv preprint arXiv:1909.06335 (2019).

class FedAvgMServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

Hsu, Tzu-Ming Harry, Hang Qi, and Matthew Brown. “Measuring the effects of non-identical data distribution for federated visual classification.” arXiv preprint arXiv:1909.06335 (2019).

property num_clients_per_round#

setup_optim(sampler, args)#: Override this function to load your optimization hyperparameters.

sample_clients(num_to_sample=None)#: Return a list of client rank indices selected randomly. The client ID is from 0 to self.num_clients -1.

global_update(buffer)#

feddyn#

Module Contents#

`FedDynServerHandler`	FedAvg server handler.
`FedDynSerialClientTrainer`	Train multiple clients in a single process.

class FedDynServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedAvg server handler.

setup_optim(alpha)#: Override this function to load your optimization hyperparameters.

global_update(buffer)#

class FedDynSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

setup_dataset(dataset)#: Override this function to set up local dataset for clients

setup_optim(epochs, batch_size, lr, alpha)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(id, model_parameters, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

fednova#

Module Contents#

`FedNovaServerHandler`	FedAvg server handler.
`FedNovaSerialClientTrainer`	Federated client with local SGD solver.

class FedNovaServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedAvg server handler.

setup_optim(option='weighted_scale')#: Override this function to load your optimization hyperparameters.

global_update(buffer)#

class FedNovaSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Federated client with local SGD solver.

local_process(payload, id_list)#: Define the local main process.

fedopt#

Module Contents#

FedOptServerHandler

FedAvg server handler.

class FedOptServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.fedavg.FedAvgServerHandler

FedAvg server handler.

property num_clients_per_round#

setup_optim(sampler, args)#: Override this function to load your optimization hyperparameters.

local_process(payload, id_list)#

global_update(buffer)#

fedprox#

Module Contents#

`FedProxServerHandler`	FedProx server handler.
`FedProxClientTrainer`	Federated client with local SGD with proximal term solver.
`FedProxSerialClientTrainer`	Train multiple clients in a single process.

class FedProxServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedProx server handler.

class FedProxClientTrainer(model: torch.nn.Module, cuda: bool = False, device: str = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_client.SGDClientTrainer

Federated client with local SGD with proximal term solver.

setup_optim(epochs, batch_size, lr, mu)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id)#

Manager of the upper layer will call this function with accepted payload

In synchronous mode, return True to end current FL round.

train(model_parameters, train_loader, mu) → None#

Client trains its local model on local dataset.

Parameters:: model_parameters (torch.Tensor) – Serialized model parameters.

class FedProxSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

setup_optim(epochs, batch_size, lr, mu)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(model_parameters, train_loader, mu) → None#

Client trains its local model on local dataset.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.
mu (float) – parameter of FedProx.

ifca#

Module Contents#

`IFCAServerHander`	Synchronous Parameter Server Handler.
`IFCASerialClientTrainer`	Train multiple clients in a single process.

class IFCAServerHander(model: torch.nn.Module, global_round: int, sample_ratio: float, cuda: bool = False, device: str = None, logger=None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

Synchronous Parameter Server Handler.

Backend of synchronous parameter server: this class is responsible for backend computing in synchronous server.

Synchronous parameter server will wait for every client to finish local training process before the next FL round.

Details in paper: http://proceedings.mlr.press/v54/mcmahan17a.html

Parameters:

model (torch.nn.Module) – model trained by federated learning.
global_round (int) – stop condition. Shut down FL system when global round is reached.
num_clients (int) – number of clients in FL. Default: 0 (initialized external).
sample_ratio (float) – the result of sample_ratio * num_clients is the number of clients for every FL round.
cuda (bool) – use GPUs or not. Default: False.
device (str, optional) – assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
sampler (FedSampler, optional) – assign a sampler to define the client sampling strategy. Default: random sampling with FedSampler.
logger (Logger, optional) – object of Logger.

property downlink_package#: Property for manager layer. Server manager will call this property when activates clients.

setup_optim(share_size, k, init_parameters)#

_summary_

Parameters:

share_size (_type_) – _description_
k (_type_) – _description_
init_parameters (_type_) – _description_

global_update(buffer)#

class IFCASerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

setup_dataset(dataset)#: Override this function to set up local dataset for clients

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

powerofchoice#

Module Contents#

`PowerofchoicePipeline`
`Powerofchoice`	Synchronous Parameter Server Handler.
`PowerofchoiceSerialClientTrainer`	Train multiple clients in a single process.

class PowerofchoicePipeline(handler: fedlab.core.server.handler.ServerHandler, trainer: fedlab.core.client.trainer.SerialClientTrainer)#

Bases: fedlab.core.standalone.StandalonePipeline

main()#

class Powerofchoice(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

Synchronous Parameter Server Handler.

Backend of synchronous parameter server: this class is responsible for backend computing in synchronous server.

Synchronous parameter server will wait for every client to finish local training process before the next FL round.

Details in paper: http://proceedings.mlr.press/v54/mcmahan17a.html

Parameters:

model (torch.nn.Module) – model trained by federated learning.
global_round (int) – stop condition. Shut down FL system when global round is reached.
num_clients (int) – number of clients in FL. Default: 0 (initialized external).
sample_ratio (float) – the result of sample_ratio * num_clients is the number of clients for every FL round.
cuda (bool) – use GPUs or not. Default: False.
device (str, optional) – assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
sampler (FedSampler, optional) – assign a sampler to define the client sampling strategy. Default: random sampling with FedSampler.
logger (Logger, optional) – object of Logger.

setup_optim(d)#: Override this function to load your optimization hyperparameters.

sample_candidates()#

sample_clients(candidates, losses)#: Return a list of client rank indices selected randomly. The client ID is from 0 to self.num_clients -1.

class PowerofchoiceSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

evaluate(id_list, model_parameters)#: Evaluate quality of local model.

qfedavg#

Module Contents#

`qFedAvgServerHandler`	qFedAvg server handler.
`qFedAvgClientTrainer`	Federated client with modified upload package and local SGD solver.

class qFedAvgServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

qFedAvg server handler.

global_update(buffer)#

class qFedAvgClientTrainer(model: torch.nn.Module, cuda: bool = False, device: str = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_client.SGDClientTrainer

Federated client with modified upload package and local SGD solver.

property uplink_package#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

setup_optim(epochs, batch_size, lr, q)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

train(model_parameters, train_loader) → None#: Client trains its local model on local dataset. :param model_parameters: Serialized model parameters. :type model_parameters: torch.Tensor

scaffold#

Module Contents#

`ScaffoldServerHandler`	FedAvg server handler.
`ScaffoldSerialClientTrainer`	Train multiple clients in a single process.

class ScaffoldServerHandler(model: torch.nn.Module, global_round: int, num_clients: int = 0, sample_ratio: float = 1, cuda: bool = False, device: str = None, sampler: fedlab.contrib.client_sampler.base_sampler.FedSampler = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedAvg server handler.

property downlink_package#: Property for manager layer. Server manager will call this property when activates clients.

setup_optim(lr)#: Override this function to load your optimization hyperparameters.

global_update(buffer)#

class ScaffoldSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(id, model_parameters, global_c, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

utils_algorithms#

Module Contents#

MinNormSolver

class MinNormSolver#

MAX_ITER = 1000#

STOP_CRIT = 1e-05#

_min_norm_element_from2(v1v2, v2v2)#: Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2 d is the distance (objective) optimzed v1v1 = <x1,x1> v1v2 = <x1,x2> v2v2 = <x2,x2>

_min_norm_2d(dps)#: Find the minimum norm solution as combination of two points This is correct only in 2D ie. min_c |sum c_i x_i|_2^2 st. sum c_i = 1 , 1 >= c_1 >= 0 for all i, c_i + c_j = 1.0 for some i, j

_min_norm_2d_accelerated(dps)#

_projection2simplex()#: Given y, it solves argmin_z |y-z|_2 st sum z = 1 , 1 >= z_i >= 0 for all i

_next_point(grad, n)#

find_min_norm_element()#: Given a list of vectors (vecs), this method finds the minimum norm element in the convex hull as min |u|_2 st. u = sum c_i vecs[i] and sum c_i = 1. It is quite geometric, and the main idea is the fact that if d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j; the solution lies in (0, d_{i,j}) Hence, we find the best 2-task solution, and then run the projected gradient descent until convergence

find_min_norm_element_FW()#: Given a list of vectors (vecs), this method finds the minimum norm element in the convex hull as min |u|_2 st. u = sum c_i vecs[i] and sum c_i = 1. It is quite geometric, and the main idea is the fact that if d_{ij} = min |u|_2 st u = c x_i + (1-c) x_j; the solution lies in (0, d_{i,j}) Hence, we find the best 2-task solution, and then run the Frank Wolfe until convergence

Package Contents#

`SGDClientTrainer`	Client backend handler, this class provides data process method to upper layer.
`SGDSerialClientTrainer`	Train multiple clients in a single process.
`SyncServerHandler`	Synchronous Parameter Server Handler.
`AsyncServerHandler`	Asynchronous Parameter Server Handler
`DittoSerialClientTrainer`	Train multiple clients in a single process.
`DittoServerHandler`	Ditto server acts the same as fedavg server.
`FedAvgSerialClientTrainer`	Federated client with local SGD solver.
`FedAvgServerHandler`	FedAvg server handler.
`FedDynSerialClientTrainer`	Train multiple clients in a single process.
`FedDynServerHandler`	FedAvg server handler.
`FedNovaSerialClientTrainer`	Federated client with local SGD solver.
`FedNovaServerHandler`	FedAvg server handler.
`FedProxSerialClientTrainer`	Train multiple clients in a single process.
`FedProxClientTrainer`	Federated client with local SGD with proximal term solver.
`FedProxServerHandler`	FedProx server handler.
`IFCASerialClientTrainer`	Train multiple clients in a single process.
`IFCAServerHander`	Synchronous Parameter Server Handler.
`PowerofchoiceSerialClientTrainer`	Train multiple clients in a single process.
`PowerofchoicePipeline`
`Powerofchoice`	Synchronous Parameter Server Handler.
`qFedAvgClientTrainer`	Federated client with modified upload package and local SGD solver.
`qFedAvgServerHandler`	qFedAvg server handler.
`ScaffoldSerialClientTrainer`	Train multiple clients in a single process.
`ScaffoldServerHandler`	FedAvg server handler.

class SGDClientTrainer(model: torch.nn.Module, cuda: bool = False, device: str = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.core.client.trainer.ClientTrainer

Client backend handler, this class provides data process method to upper layer.

Parameters:

model (torch.nn.Module) – PyTorch model.
cuda (bool, optional) – use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – :object of Logger.

property uplink_package#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

setup_dataset(dataset)#: Set up local dataset self.dataset for clients.

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id)#

Manager of the upper layer will call this function with accepted payload

In synchronous mode, return True to end current FL round.

train(model_parameters, train_loader) → None#

Client trains its local model on local dataset.

Parameters:: model_parameters (torch.Tensor) – Serialized model parameters.

class SGDSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.core.client.trainer.SerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

property uplink_package#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

setup_dataset(dataset)#: Override this function to set up local dataset for clients

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(model_parameters, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

Bases: fedlab.core.server.handler.ServerHandler

Synchronous Parameter Server Handler.

Backend of synchronous parameter server: this class is responsible for backend computing in synchronous server.

Synchronous parameter server will wait for every client to finish local training process before the next FL round.

Details in paper: http://proceedings.mlr.press/v54/mcmahan17a.html

Parameters:

model (torch.nn.Module) – model trained by federated learning.
global_round (int) – stop condition. Shut down FL system when global round is reached.
num_clients (int) – number of clients in FL. Default: 0 (initialized external).
sample_ratio (float) – the result of sample_ratio * num_clients is the number of clients for every FL round.
cuda (bool) – use GPUs or not. Default: False.
device (str, optional) – assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
sampler (FedSampler, optional) – assign a sampler to define the client sampling strategy. Default: random sampling with FedSampler.
logger (Logger, optional) – object of Logger.

property downlink_package: List[torch.Tensor]#: Property for manager layer. Server manager will call this property when activates clients.

property num_clients_per_round#

property if_stop#: NetworkManager keeps monitoring this attribute, and it will stop all related processes and threads when True returned.

sample_clients(num_to_sample=None)#: Return a list of client rank indices selected randomly. The client ID is from 0 to self.num_clients -1.

global_update(buffer)#

load(payload: List[torch.Tensor]) → bool#

Update global model with collected parameters from clients.

Note

Parameters:: payload (list[torch.Tensor]) – A list of tensors passed by manager layer.

class AsyncServerHandler(model: torch.nn.Module, global_round: int, num_clients: int, cuda: bool = False, device: str = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.core.server.handler.ServerHandler

Asynchronous Parameter Server Handler

Update global model immediately after receiving a ParameterUpdate message Paper: https://arxiv.org/abs/1903.03934

Parameters:

model (torch.nn.Module) – Global model in server
global_round (int) – stop condition. Shut down FL system when global round is reached.
num_clients (int) – number of clients in FL.
cuda (bool) – Use GPUs or not.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
logger (Logger, optional) – Object of Logger.

property if_stop#: NetworkManager keeps monitoring this attribute, and it will stop all related processes and threads when True returned.

property downlink_package#: Property for manager layer. Server manager will call this property when activates clients.

setup_optim(alpha, strategy='constant', a=10, b=4)#

Setup optimization configuration.

Parameters:

alpha (float) – Weight used in async aggregation.
strategy (str, optional) – Adaptive strategy. constant, hinge and polynomial is optional. Default: constant.. Defaults to ‘constant’.
a (int, optional) – Parameter used in async aggregation.. Defaults to 10.
b (int, optional) – Parameter used in async aggregation.. Defaults to 4.

global_update(buffer)#

load(payload: List[torch.Tensor]) → bool#: Override this function to define how to update global model (aggregation or optimization).

adapt_alpha(receive_model_time)#: update the alpha according to staleness

class DittoSerialClientTrainer(model, num, cuda=False, device=None, logger=None, personal=True)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

property uplink_package#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

setup_dataset(dataset)#: Override this function to set up local dataset for clients

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(global_model_parameters, local_model_parameters, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

Ditto server acts the same as fedavg server.

class FedAvgSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Federated client with local SGD solver.

train(model_parameters, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedAvg server handler.

global_update(buffer)#

class FedDynSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

setup_dataset(dataset)#: Override this function to set up local dataset for clients

setup_optim(epochs, batch_size, lr, alpha)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(id, model_parameters, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedAvg server handler.

setup_optim(alpha)#: Override this function to load your optimization hyperparameters.

global_update(buffer)#

class FedNovaSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Federated client with local SGD solver.

local_process(payload, id_list)#: Define the local main process.

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedAvg server handler.

setup_optim(option='weighted_scale')#: Override this function to load your optimization hyperparameters.

global_update(buffer)#

class FedProxSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

setup_optim(epochs, batch_size, lr, mu)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(model_parameters, train_loader, mu) → None#

Client trains its local model on local dataset.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.
mu (float) – parameter of FedProx.

class FedProxClientTrainer(model: torch.nn.Module, cuda: bool = False, device: str = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_client.SGDClientTrainer

Federated client with local SGD with proximal term solver.

setup_optim(epochs, batch_size, lr, mu)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id)#

Manager of the upper layer will call this function with accepted payload

In synchronous mode, return True to end current FL round.

train(model_parameters, train_loader, mu) → None#

Client trains its local model on local dataset.

Parameters:: model_parameters (torch.Tensor) – Serialized model parameters.

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedProx server handler.

class IFCASerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

setup_dataset(dataset)#: Override this function to set up local dataset for clients

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

class IFCAServerHander(model: torch.nn.Module, global_round: int, sample_ratio: float, cuda: bool = False, device: str = None, logger=None)#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

Synchronous Parameter Server Handler.

Backend of synchronous parameter server: this class is responsible for backend computing in synchronous server.

Synchronous parameter server will wait for every client to finish local training process before the next FL round.

Details in paper: http://proceedings.mlr.press/v54/mcmahan17a.html

Parameters:

model (torch.nn.Module) – model trained by federated learning.
global_round (int) – stop condition. Shut down FL system when global round is reached.
num_clients (int) – number of clients in FL. Default: 0 (initialized external).
sample_ratio (float) – the result of sample_ratio * num_clients is the number of clients for every FL round.
cuda (bool) – use GPUs or not. Default: False.
device (str, optional) – assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
sampler (FedSampler, optional) – assign a sampler to define the client sampling strategy. Default: random sampling with FedSampler.
logger (Logger, optional) – object of Logger.

property downlink_package#: Property for manager layer. Server manager will call this property when activates clients.

setup_optim(share_size, k, init_parameters)#

_summary_

Parameters:

share_size (_type_) – _description_
k (_type_) – _description_
init_parameters (_type_) – _description_

global_update(buffer)#

class PowerofchoiceSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

evaluate(id_list, model_parameters)#: Evaluate quality of local model.

class PowerofchoicePipeline(handler: fedlab.core.server.handler.ServerHandler, trainer: fedlab.core.client.trainer.SerialClientTrainer)#

Bases: fedlab.core.standalone.StandalonePipeline

main()#

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

Synchronous Parameter Server Handler.

Backend of synchronous parameter server: this class is responsible for backend computing in synchronous server.

Synchronous parameter server will wait for every client to finish local training process before the next FL round.

Details in paper: http://proceedings.mlr.press/v54/mcmahan17a.html

Parameters:

model (torch.nn.Module) – model trained by federated learning.
global_round (int) – stop condition. Shut down FL system when global round is reached.
num_clients (int) – number of clients in FL. Default: 0 (initialized external).
sample_ratio (float) – the result of sample_ratio * num_clients is the number of clients for every FL round.
cuda (bool) – use GPUs or not. Default: False.
device (str, optional) – assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.
sampler (FedSampler, optional) – assign a sampler to define the client sampling strategy. Default: random sampling with FedSampler.
logger (Logger, optional) – object of Logger.

setup_optim(d)#: Override this function to load your optimization hyperparameters.

sample_candidates()#

sample_clients(candidates, losses)#: Return a list of client rank indices selected randomly. The client ID is from 0 to self.num_clients -1.

class qFedAvgClientTrainer(model: torch.nn.Module, cuda: bool = False, device: str = None, logger: fedlab.utils.Logger = None)#

Bases: fedlab.contrib.algorithm.basic_client.SGDClientTrainer

Federated client with modified upload package and local SGD solver.

property uplink_package#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

setup_optim(epochs, batch_size, lr, q)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

train(model_parameters, train_loader) → None#: Client trains its local model on local dataset. :param model_parameters: Serialized model parameters. :type model_parameters: torch.Tensor

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

qFedAvg server handler.

global_update(buffer)#

class ScaffoldSerialClientTrainer(model, num_clients, cuda=False, device=None, logger=None, personal=False)#

Bases: fedlab.contrib.algorithm.basic_client.SGDSerialClientTrainer

Train multiple clients in a single process.

Customize _get_dataloader() or _train_alone() for specific algorithm design in clients.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
logger (Logger, optional) – Object of Logger.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

setup_optim(epochs, batch_size, lr)#

Set up local optimization configuration.

Parameters:

epochs (int) – Local epochs.
batch_size (int) – Local batch size.
lr (float) – Learning rate.

local_process(payload, id_list)#: Define the local main process.

train(id, model_parameters, global_c, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters:

model_parameters (torch.Tensor) – serialized model parameters.
train_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.

Bases: fedlab.contrib.algorithm.basic_server.SyncServerHandler

FedAvg server handler.

property downlink_package#: Property for manager layer. Server manager will call this property when activates clients.

setup_optim(lr)#: Override this function to load your optimization hyperparameters.

global_update(buffer)#

client_sampler#

base_sampler#

Module Contents#

FedSampler

class FedSampler(n)[source]#

__metaclass__#

abstract candidate(size)[source]#

abstract sample(size)[source]#

abstract update(val)[source]#

divfl#

importance_sampler#

Module Contents#

`MultiArmedBanditSampler`	Refer to [Stochastic Optimization with Bandit Sampling](https://arxiv.org/abs/1708.02544).
`OptimalSampler`	Refer to [Optimal Client Sampling for Federated Learning](arxiv.org/abs/2010.13723).

class MultiArmedBanditSampler(n, T, L)#

Bases: fedlab.contrib.client_sampler.base_sampler.FedSampler

Refer to [Stochastic Optimization with Bandit Sampling](https://arxiv.org/abs/1708.02544).

sample(batch_size)#

update(loss)#

class OptimalSampler(n, k)#

Bases: fedlab.contrib.client_sampler.base_sampler.FedSampler

Refer to [Optimal Client Sampling for Federated Learning](arxiv.org/abs/2010.13723).

sample(size=None)#

update(loss)#

optim_solver(norms)#

mabs#

power_of_choice#

uniform_sampler#

Module Contents#

RandomSampler

class RandomSampler(n, probs=None)#

Bases: fedlab.contrib.client_sampler.base_sampler.FedSampler

sample(k, replace=False)#

update(probs)#

vrb#

compressor#

Module Contents#

Compressor

Helper class that provides a standard way to create an ABC using

class Compressor#

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

abstract compress(*args, **kwargs)#

abstract decompress(*args, **kwargs)#

quantization#

Module Contents#

QSGDCompressor

Quantization compressor.

class QSGDCompressor(n_bit, random=True, cuda=False)#

Bases: fedlab.contrib.compressor.compressor.Compressor

Quantization compressor.

A implementation for paper https://proceedings.neurips.cc/paper/2017/file/6c340f25839e6acdc73414517203f5f0-Paper.pdf.

Alistarh, Dan, et al. “QSGD: Communication-efficient SGD via gradient quantization and encoding.” Advances in Neural Information Processing Systems 30 (2017): 1709-1720. Thanks to git repo: https://github.com/xinyandai/gradient-quantization

Parameters:

n_bit (int) – the bits num for quantization. Bigger n_bit comes with better compress precision but more communication consumption.
random (bool, optional) – Carry bit with probability. Defaults to True.
cuda (bool, optional) – use GPU. Defaults to False.

compress(tensor)#

Compress a tensor with quantization :param tensor: [description] :type tensor: [type]

Returns:: The normalization number. signs (torch.Tensor): Tensor that indicates the sign of coresponding number. quantized_intervals (torch.Tensor): Quantized tensor that each item in [0, 2**n_bit -1].
Return type:: norm (torch.Tensor)

decompress(signature)#

Decompress tensor :param signature: [norm, signs, quantized_intervals], returned by :func:compress. :type signature: list

Returns:: Raw tensor represented by signature.
Return type:: torch.Tensor

topk#

Module Contents#

TopkCompressor

Compressor for federated communication

class TopkCompressor(compress_ratio)#

Bases: fedlab.contrib.compressor.compressor.Compressor

Compressor for federated communication Top-k gradient or weights selection :param compress_ratio: compress ratio :type compress_ratio: float

compress(tensor)#

compress tensor into (values, indices) :param tensor: tensor :type tensor: torch.Tensor

Returns:: (values, indices)
Return type:: tuple

decompress(values, indices, shape)#: decompress tensor

Package Contents#

`QSGDCompressor`	Quantization compressor.
`TopkCompressor`	Compressor for federated communication

class QSGDCompressor(n_bit, random=True, cuda=False)#

Bases: fedlab.contrib.compressor.compressor.Compressor

Quantization compressor.

A implementation for paper https://proceedings.neurips.cc/paper/2017/file/6c340f25839e6acdc73414517203f5f0-Paper.pdf.

Parameters:

n_bit (int) – the bits num for quantization. Bigger n_bit comes with better compress precision but more communication consumption.
random (bool, optional) – Carry bit with probability. Defaults to True.
cuda (bool, optional) – use GPU. Defaults to False.

compress(tensor)#

Compress a tensor with quantization :param tensor: [description] :type tensor: [type]

Returns:: The normalization number. signs (torch.Tensor): Tensor that indicates the sign of coresponding number. quantized_intervals (torch.Tensor): Quantized tensor that each item in [0, 2**n_bit -1].
Return type:: norm (torch.Tensor)

decompress(signature)#

Decompress tensor :param signature: [norm, signs, quantized_intervals], returned by :func:compress. :type signature: list

Returns:: Raw tensor represented by signature.
Return type:: torch.Tensor

class TopkCompressor(compress_ratio)#

Bases: fedlab.contrib.compressor.compressor.Compressor

Compressor for federated communication Top-k gradient or weights selection :param compress_ratio: compress ratio :type compress_ratio: float

compress(tensor)#

compress tensor into (values, indices) :param tensor: tensor :type tensor: torch.Tensor

Returns:: (values, indices)
Return type:: tuple

decompress(values, indices, shape)#: decompress tensor

dataset#

adult#

Module Contents#

Adult

Adult dataset from LIBSVM Data.

class Adult(root, train=True, transform=None, target_transform=None, download=False)#

Bases: torch.utils.data.Dataset

Adult dataset from LIBSVM Data.

Parameters:

root (str) – Root directory of raw dataset to download if download is set to True.
train (bool, optional) – If True, creates dataset from training set, otherwise creates from test set.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version. Default as None.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it. Default as None.
download (bool, optional) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

url = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/'#

train_file_name = 'a9a'#

test_file_name = 'a9a.t'#

num_classes = 2#

num_features = 123#

download()#

_local_file_existence()#

__getitem__(index)#

Parameters:: index (int) – Index
Returns:: (features, target) where target is index of the target class.
Return type:: tuple

__len__()#

extra_repr() → str#

basic_dataset#

Module Contents#

`BaseDataset`	Base dataset iterator
`Subset`	For data subset with different augmentation for different client.
`CIFARSubset`	For data subset with different augmentation for different client.
`FedDataset`

class BaseDataset(x, y)#

Bases: torch.utils.data.Dataset

Base dataset iterator

__len__()#

__getitem__(index)#

class Subset(dataset, indices, transform=None, target_transform=None)#

Bases: torch.utils.data.Dataset

For data subset with different augmentation for different client.

Parameters:

dataset (Dataset) – The whole Dataset
indices (List[int]) – Indices of sub-dataset to achieve from dataset.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

__getitem__(index)#

Get item

Parameters:: index (int) – index
Returns:: (image, target) where target is index of the target class.

__len__()#

class CIFARSubset(dataset, indices, transform=None, target_transform=None, to_image=True)#

Bases: Subset

For data subset with different augmentation for different client.

Parameters:

dataset (Dataset) – The whole Dataset
indices (List[int]) – Indices of sub-dataset to achieve from dataset.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

class FedDataset#

Bases: object

preprocess()#: Define the dataset partition process

abstract get_dataset(id, type='train')#

Get dataset class

Parameters:

id (int) – Client ID for the partial dataset to achieve.
type (str, optional) – Type of dataset, can be chosen from ["train", "val", "test"]. Defaults as "train".

Raises:

NotImplementedError –

abstract get_dataloader(id, batch_size, type='train')#: Get data loader

__len__()#

celeba#

Module Contents#

CelebADataset

class CelebADataset(client_id: int, client_str: str, data: list, targets: list, image_root: str, transform=None)#

Bases: torch.utils.data.Dataset

_process_data_target()#: process client’s data and target

__len__()#

__getitem__(index)#

covtype#

Module Contents#

Covtype

Covtype binary dataset from LIBSVM Data.

class Covtype(root, train=True, train_ratio=0.75, transform=None, target_transform=None, download=False, generate=False, seed=None)#

Bases: torch.utils.data.Dataset

Covtype binary dataset from LIBSVM Data.

Parameters:

root (str) – Root directory of raw dataset to download if download is set to True.
train (bool, optional) – If True, creates dataset from training set, otherwise creates from test set.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version. Default as None.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it. Default as None.
download (bool, optional) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

num_classes = 2#

num_features = 54#

url = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/covtype.libsvm.binary.bz2'#

source_file_name = 'covtype.libsvm.binary.bz2'#

download()#

generate()#

_local_npy_existence()#

_local_source_file_existence()#

__getitem__(index)#

Parameters:: index (int) – Index
Returns:: (features, target) where target is index of the target class.
Return type:: tuple

__len__()#

fcube#

Module Contents#

FCUBE

FCUBE data set.

class FCUBE(root, train=True, generate=True, transform=None, target_transform=None, num_samples=4000)#

Bases: torch.utils.data.Dataset

FCUBE data set.

From paper Federated Learning on Non-IID Data Silos: An Experimental Study.

Parameters:

root (str) – Root for data file.
train (bool, optional) – Training set or test set. Default as True.
generate (bool, optional) – Whether to generate synthetic dataset. If True, then generate new synthetic FCUBE data even existed. Default as True.
transform (callable, optional) – A function/transform that takes in an numpy.ndarray and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.
num_samples (int, optional) – Total number of samples to generate. We suggest to use 4000 for training set, and 1000 for test set. Default is 4000 for trainset.

train_files#

test_files#

num_clients = 4#

_generate_train()#

_generate_test()#

_save_data()#

__len__()#

__getitem__(index)#

Parameters:: index (int) – Index
Returns:: (features, target) where target is index of the target class.
Return type:: tuple

femnist#

Module Contents#

FemnistDataset

class FemnistDataset(client_id: int, client_str: str, data: list, targets: list)#

Bases: torch.utils.data.Dataset

_process_data_target()#: process client’s data and target

__len__()#

__getitem__(index)#

partitioned_cifar#

Module Contents#

PartitionCIFAR

FedDataset with partitioning preprocess. For detailed partitioning, please

class PartitionCIFAR(root, path, dataname, num_clients, download=True, preprocess=False, balance=True, partition='iid', unbalance_sgm=0, num_shards=None, dir_alpha=None, verbose=True, seed=None, transform=None, target_transform=None)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

FedDataset with partitioning preprocess. For detailed partitioning, please check Federated Dataset and DataPartitioner.

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
dataname (str) – “cifar10” or “cifar100”
num_clients (int) – Number of clients.
download (bool) – Whether to download the raw dataset.
preprocess (bool) – Whether to preprocess the dataset.
balance (bool, optional) – Balanced partition over all clients or not. Default as True.
partition (str, optional) – Partition type, only "iid", shards, "dirichlet" are supported. Default as "iid".
unbalance_sgm (float, optional) – Log-normal distribution variance for unbalanced data partition over clients. Default as 0 for balanced partition.
num_shards (int, optional) – Number of shards in non-iid "shards" partition. Only works if partition="shards". Default as None.
dir_alpha (float, optional) – Dirichlet distribution parameter for non-iid partition. Only works if partition="dirichlet". Default as None.
verbose (bool, optional) – Whether to print partition process. Default as True.
seed (int, optional) – Random seed. Default as None.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

preprocess(balance=True, partition='iid', unbalance_sgm=0, num_shards=None, dir_alpha=None, verbose=True, seed=None, download=True)#

Perform FL partition on the dataset, and save each subset for each client into data{cid}.pkl file.

For details of partition schemes, please check Federated Dataset and DataPartitioner.

get_dataset(cid, type='train')#

Load subdataset for client with client ID cid from local file.

Parameters:

cid (int) – client id
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

Returns:

Dataset

get_dataloader(cid, batch_size=None, type='train')#

Return dataload for client with client ID cid.

Parameters:

cid (int) – client id
batch_size (int, optional) – batch size in DataLoader.
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

partitioned_cifar10#

Module Contents#

PartitionedCIFAR10

FedDataset with partitioning preprocess. For detailed partitioning, please

class PartitionedCIFAR10(root, path, dataname, num_clients, download=True, preprocess=False, balance=True, partition='iid', unbalance_sgm=0, num_shards=None, dir_alpha=None, verbose=True, seed=None, transform=None, target_transform=None)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

FedDataset with partitioning preprocess. For detailed partitioning, please check Federated Dataset and DataPartitioner.

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
dataname (str) – “cifar10” or “cifar100”
num_clients (int) – Number of clients.
download (bool) – Whether to download the raw dataset.
preprocess (bool) – Whether to preprocess the dataset.
balance (bool, optional) – Balanced partition over all clients or not. Default as True.
partition (str, optional) – Partition type, only "iid", shards, "dirichlet" are supported. Default as "iid".
unbalance_sgm (float, optional) – Log-normal distribution variance for unbalanced data partition over clients. Default as 0 for balanced partition.
num_shards (int, optional) – Number of shards in non-iid "shards" partition. Only works if partition="shards". Default as None.
dir_alpha (float, optional) – Dirichlet distribution parameter for non-iid partition. Only works if partition="dirichlet". Default as None.
verbose (bool, optional) – Whether to print partition process. Default as True.
seed (int, optional) – Random seed. Default as None.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

preprocess(balance=True, partition='iid', unbalance_sgm=0, num_shards=None, dir_alpha=None, verbose=True, seed=None, download=True)#

Perform FL partition on the dataset, and save each subset for each client into data{cid}.pkl file.

For details of partition schemes, please check Federated Dataset and DataPartitioner.

get_dataset(cid, type='train')#

Load subdataset for client with client ID cid from local file.

Parameters:

cid (int) – client id
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

Returns:

Dataset

get_dataloader(cid, batch_size=None, type='train')#

Return dataload for client with client ID cid.

Parameters:

cid (int) – client id
batch_size (int, optional) – batch size in DataLoader.
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

partitioned_mnist#

Module Contents#

PartitionedMNIST

FedDataset with partitioning preprocess. For detailed partitioning, please

class PartitionedMNIST(root, path, num_clients, download=True, preprocess=False, partition='iid', dir_alpha=None, verbose=True, seed=None, transform=None, target_transform=None)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

FedDataset with partitioning preprocess. For detailed partitioning, please check Federated Dataset and DataPartitioner.

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
num_clients (int) – Number of clients.
download (bool) – Whether to download the raw dataset.
preprocess (bool) – Whether to preprocess the dataset.
partition (str, optional) – Partition name. Only supports "noniid-#label", "noniid-labeldir", "unbalance" and "iid" partition schemes.
dir_alpha (float, optional) – Dirichlet distribution parameter for non-iid partition. Only works if partition="dirichlet". Default as None.
verbose (bool, optional) – Whether to print partition process. Default as True.
seed (int, optional) – Random seed. Default as None.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

preprocess(partition='iid', dir_alpha=None, verbose=True, seed=None, download=True, transform=None, target_transform=None)#

Perform FL partition on the dataset, and save each subset for each client into data{cid}.pkl file.

For details of partition schemes, please check Federated Dataset and DataPartitioner.

get_dataset(cid, type='train')#

Load subdataset for client with client ID cid from local file.

Parameters:

cid (int) – client id
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

Returns:

Dataset

get_dataloader(cid, batch_size=None, type='train')#

Return dataload for client with client ID cid.

Parameters:

cid (int) – client id
batch_size (int, optional) – batch size in DataLoader.
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

pathological_mnist#

Module Contents#

PathologicalMNIST

The partition stratigy in FedAvg. See http://proceedings.mlr.press/v54/mcmahan17a?ref=https://githubhelp.com

class PathologicalMNIST(root, path, num_clients=100, shards=200, download=True, preprocess=False)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

The partition stratigy in FedAvg. See http://proceedings.mlr.press/v54/mcmahan17a?ref=https://githubhelp.com

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
num_clients (int) – Number of clients.
shards (int, optional) – Sort the dataset by the label, and uniformly partition them into shards. Then
download (bool, optional) – Download. Defaults to True.

preprocess(download=True)#: Define the dataset partition process

get_dataset(id, type='train')#

Load subdataset for client with client ID cid from local file.

Parameters:

cid (int) – client id
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

Returns:

Dataset

get_dataloader(id, batch_size=None, type='train')#

Return dataload for client with client ID cid.

Parameters:

cid (int) – client id
batch_size (int, optional) – batch size in DataLoader.
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

rcv1#

Module Contents#

RCV1

RCV1 binary dataset from LIBSVM Data.

class RCV1(root, train=True, train_ratio=0.75, transform=None, target_transform=None, download=False, generate=False, seed=None)#

Bases: torch.utils.data.Dataset

RCV1 binary dataset from LIBSVM Data.

Parameters:

root (str) – Root directory of raw dataset to download if download is set to True.
train (bool, optional) – If True, creates dataset from training set, otherwise creates from test set.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version. Default as None.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it. Default as None.
download (bool, optional) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

num_classes = 2#

num_features = 47236#

url = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/rcv1_train.binary.bz2'#

source_file_name = 'rcv1_train.binary.bz2'#

download()#

generate()#

_local_npy_existence()#

_local_source_file_existence()#

__getitem__(index)#

Parameters:: index (int) – Index
Returns:: (features, target) where target is index of the target class.
Return type:: tuple

__len__()#

rotated_cifar10#

Module Contents#

RotatedCIFAR10

Rotate CIFAR10 and patrition them.

class RotatedCIFAR10(root, save_dir, num_clients)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

Rotate CIFAR10 and patrition them.

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
num_clients (int) – Number of clients.

preprocess(shards, thetas=[0, 180])#

_summary_

Parameters:

shards (_type_) – _description_
thetas (list, optional) – _description_. Defaults to [0, 180].

get_dataset(id, type='train')#

Get dataset class

Parameters:

id (int) – Client ID for the partial dataset to achieve.
type (str, optional) – Type of dataset, can be chosen from ["train", "val", "test"]. Defaults as "train".

Raises:

NotImplementedError –

get_data_loader(id, batch_size=None, type='train')#

rotated_mnist#

Module Contents#

RotatedMNIST

Rotate MNIST and partition them.

class RotatedMNIST(root, path, num)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

Rotate MNIST and partition them.

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
num_clients (int) – Number of clients.

preprocess(thetas=[0, 90, 180, 270], download=True)#: Define the dataset partition process

get_dataset(id, type='train')#

Get dataset class

Parameters:

id (int) – Client ID for the partial dataset to achieve.
type (str, optional) – Type of dataset, can be chosen from ["train", "val", "test"]. Defaults as "train".

Raises:

NotImplementedError –

get_data_loader(id, batch_size=None, type='train')#

sent140#

Module Contents#

Sent140Dataset

BASE_DIR

BASE_DIR#

class Sent140Dataset(client_id: int, client_str: str, data: list, targets: list, is_to_tokens: bool = True, tokenizer: fedlab.contrib.dataset.utils.Tokenizer = None)#

Bases: torch.utils.data.Dataset

_process_data_target()#: process client’s data and target

_data2token()#

encode(vocab: fedlab.contrib.dataset.utils.Vocab, fix_len: int)#

transform token data to indices sequence by Vocab :param vocab: vocab for data_token :type vocab: fedlab_benchmark.leaf.nlp_utils.util.vocab :param fix_len: max length of sentence :type fix_len: int

Returns:: list of integer list for data_token, and a list of tensor target

__encode_tokens(tokens, pad_idx) → torch.Tensor#

encode fix_len length for token_data to get indices list in self.vocab if one sentence length is shorter than fix_len, it will use pad word for padding to fix_len if one sentence length is longer than fix_len, it will cut the first max_words words :param tokens: data after tokenizer :type tokens: list[str]

Returns:: integer list of indices with fix_len length for tokens input

__len__()#

__getitem__(item)#

shakespeare#

Module Contents#

ShakespeareDataset

class ShakespeareDataset(client_id: int, client_str: str, data: list, targets: list)#

Bases: torch.utils.data.Dataset

_build_vocab()#: according all letters to build vocab Vocabulary re-used from the Federated Learning for Text Generation tutorial. https://www.tensorflow.org/federated/tutorials/federated_learning_for_text_generation :returns: all letters vocabulary list and length of vocab list

_process_data_target()#: process client’s data and target

__sentence_to_indices(sentence: str)#

Returns list of integer for character indices in ALL_LETTERS :param sentence: input sentence :type sentence: str

Returns: a integer list of character indices

__letter_to_index(letter: str)#

Returns index in ALL_LETTERS of given letter :param letter: input letter :type letter: char/str[0]

Returns: int index of input letter

__len__()#

__getitem__(index)#

synthetic_dataset#

Module Contents#

SyntheticDataset

class SyntheticDataset(root, path, preprocess=False)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

preprocess(root, path, partition=0.2)#

Preprocess the raw data to fedlab dataset format.

Parameters:

root (str) – path to the raw data.
path (str) – path to save the preprocessed datasets.
partition (float, optional) – The propotion of testset. Defaults to 0.2.

get_dataset(id, type='train')#

Get dataset class

Parameters:

id (int) – Client ID for the partial dataset to achieve.
type (str, optional) – Type of dataset, can be chosen from ["train", "val", "test"]. Defaults as "train".

Raises:

NotImplementedError –

get_dataloader(id, batch_size, type='train')#: Get data loader

Package Contents#

`FedDataset`
`BaseDataset`	Base dataset iterator
`Subset`	For data subset with different augmentation for different client.
`FCUBE`	FCUBE data set.
`Covtype`	Covtype binary dataset from LIBSVM Data.
`RCV1`	RCV1 binary dataset from LIBSVM Data.
`PathologicalMNIST`	The partition stratigy in FedAvg. See http://proceedings.mlr.press/v54/mcmahan17a?ref=https://githubhelp.com
`RotatedMNIST`	Rotate MNIST and partition them.
`RotatedCIFAR10`	Rotate CIFAR10 and patrition them.
`PartitionedMNIST`	`FedDataset` with partitioning preprocess. For detailed partitioning, please
`PartitionedCIFAR10`	`FedDataset` with partitioning preprocess. For detailed partitioning, please
`SyntheticDataset`

class FedDataset#

Bases: object

preprocess()#: Define the dataset partition process

abstract get_dataset(id, type='train')#

Get dataset class

Parameters:

id (int) – Client ID for the partial dataset to achieve.
type (str, optional) – Type of dataset, can be chosen from ["train", "val", "test"]. Defaults as "train".

Raises:

NotImplementedError –

abstract get_dataloader(id, batch_size, type='train')#: Get data loader

__len__()#

class BaseDataset(x, y)#

Bases: torch.utils.data.Dataset

Base dataset iterator

__len__()#

__getitem__(index)#

class Subset(dataset, indices, transform=None, target_transform=None)#

Bases: torch.utils.data.Dataset

For data subset with different augmentation for different client.

Parameters:

dataset (Dataset) – The whole Dataset
indices (List[int]) – Indices of sub-dataset to achieve from dataset.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

__getitem__(index)#

Get item

Parameters:: index (int) – index
Returns:: (image, target) where target is index of the target class.

__len__()#

class FCUBE(root, train=True, generate=True, transform=None, target_transform=None, num_samples=4000)#

Bases: torch.utils.data.Dataset

FCUBE data set.

From paper Federated Learning on Non-IID Data Silos: An Experimental Study.

Parameters:

root (str) – Root for data file.
train (bool, optional) – Training set or test set. Default as True.
generate (bool, optional) – Whether to generate synthetic dataset. If True, then generate new synthetic FCUBE data even existed. Default as True.
transform (callable, optional) – A function/transform that takes in an numpy.ndarray and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.
num_samples (int, optional) – Total number of samples to generate. We suggest to use 4000 for training set, and 1000 for test set. Default is 4000 for trainset.

train_files#

test_files#

num_clients = 4#

_generate_train()#

_generate_test()#

_save_data()#

__len__()#

__getitem__(index)#

Parameters:: index (int) – Index
Returns:: (features, target) where target is index of the target class.
Return type:: tuple

class Covtype(root, train=True, train_ratio=0.75, transform=None, target_transform=None, download=False, generate=False, seed=None)#

Bases: torch.utils.data.Dataset

Covtype binary dataset from LIBSVM Data.

Parameters:

root (str) – Root directory of raw dataset to download if download is set to True.
train (bool, optional) – If True, creates dataset from training set, otherwise creates from test set.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version. Default as None.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it. Default as None.
download (bool, optional) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

num_classes = 2#

num_features = 54#

url = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/covtype.libsvm.binary.bz2'#

source_file_name = 'covtype.libsvm.binary.bz2'#

download()#

generate()#

_local_npy_existence()#

_local_source_file_existence()#

__getitem__(index)#

Parameters:: index (int) – Index
Returns:: (features, target) where target is index of the target class.
Return type:: tuple

__len__()#

class RCV1(root, train=True, train_ratio=0.75, transform=None, target_transform=None, download=False, generate=False, seed=None)#

Bases: torch.utils.data.Dataset

RCV1 binary dataset from LIBSVM Data.

Parameters:

root (str) – Root directory of raw dataset to download if download is set to True.
train (bool, optional) – If True, creates dataset from training set, otherwise creates from test set.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version. Default as None.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it. Default as None.
download (bool, optional) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

num_classes = 2#

num_features = 47236#

url = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/rcv1_train.binary.bz2'#

source_file_name = 'rcv1_train.binary.bz2'#

download()#

generate()#

_local_npy_existence()#

_local_source_file_existence()#

__getitem__(index)#

Parameters:: index (int) – Index
Returns:: (features, target) where target is index of the target class.
Return type:: tuple

__len__()#

class PathologicalMNIST(root, path, num_clients=100, shards=200, download=True, preprocess=False)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

The partition stratigy in FedAvg. See http://proceedings.mlr.press/v54/mcmahan17a?ref=https://githubhelp.com

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
num_clients (int) – Number of clients.
shards (int, optional) – Sort the dataset by the label, and uniformly partition them into shards. Then
download (bool, optional) – Download. Defaults to True.

preprocess(download=True)#: Define the dataset partition process

get_dataset(id, type='train')#

Load subdataset for client with client ID cid from local file.

Parameters:

cid (int) – client id
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

Returns:

Dataset

get_dataloader(id, batch_size=None, type='train')#

Return dataload for client with client ID cid.

Parameters:

cid (int) – client id
batch_size (int, optional) – batch size in DataLoader.
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

class RotatedMNIST(root, path, num)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

Rotate MNIST and partition them.

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
num_clients (int) – Number of clients.

preprocess(thetas=[0, 90, 180, 270], download=True)#: Define the dataset partition process

get_dataset(id, type='train')#

Get dataset class

Parameters:

id (int) – Client ID for the partial dataset to achieve.
type (str, optional) – Type of dataset, can be chosen from ["train", "val", "test"]. Defaults as "train".

Raises:

NotImplementedError –

get_data_loader(id, batch_size=None, type='train')#

class RotatedCIFAR10(root, save_dir, num_clients)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

Rotate CIFAR10 and patrition them.

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
num_clients (int) – Number of clients.

preprocess(shards, thetas=[0, 180])#

_summary_

Parameters:

shards (_type_) – _description_
thetas (list, optional) – _description_. Defaults to [0, 180].

get_dataset(id, type='train')#

Get dataset class

Parameters:

id (int) – Client ID for the partial dataset to achieve.
type (str, optional) – Type of dataset, can be chosen from ["train", "val", "test"]. Defaults as "train".

Raises:

NotImplementedError –

get_data_loader(id, batch_size=None, type='train')#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

FedDataset with partitioning preprocess. For detailed partitioning, please check Federated Dataset and DataPartitioner.

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
num_clients (int) – Number of clients.
download (bool) – Whether to download the raw dataset.
preprocess (bool) – Whether to preprocess the dataset.
partition (str, optional) – Partition name. Only supports "noniid-#label", "noniid-labeldir", "unbalance" and "iid" partition schemes.
dir_alpha (float, optional) – Dirichlet distribution parameter for non-iid partition. Only works if partition="dirichlet". Default as None.
verbose (bool, optional) – Whether to print partition process. Default as True.
seed (int, optional) – Random seed. Default as None.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

preprocess(partition='iid', dir_alpha=None, verbose=True, seed=None, download=True, transform=None, target_transform=None)#

Perform FL partition on the dataset, and save each subset for each client into data{cid}.pkl file.

For details of partition schemes, please check Federated Dataset and DataPartitioner.

get_dataset(cid, type='train')#

Load subdataset for client with client ID cid from local file.

Parameters:

cid (int) – client id
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

Returns:

Dataset

get_dataloader(cid, batch_size=None, type='train')#

Return dataload for client with client ID cid.

Parameters:

cid (int) – client id
batch_size (int, optional) – batch size in DataLoader.
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

FedDataset with partitioning preprocess. For detailed partitioning, please check Federated Dataset and DataPartitioner.

Parameters:

root (str) – Path to download raw dataset.
path (str) – Path to save partitioned subdataset.
dataname (str) – “cifar10” or “cifar100”
num_clients (int) – Number of clients.
download (bool) – Whether to download the raw dataset.
preprocess (bool) – Whether to preprocess the dataset.
balance (bool, optional) – Balanced partition over all clients or not. Default as True.
partition (str, optional) – Partition type, only "iid", shards, "dirichlet" are supported. Default as "iid".
unbalance_sgm (float, optional) – Log-normal distribution variance for unbalanced data partition over clients. Default as 0 for balanced partition.
num_shards (int, optional) – Number of shards in non-iid "shards" partition. Only works if partition="shards". Default as None.
dir_alpha (float, optional) – Dirichlet distribution parameter for non-iid partition. Only works if partition="dirichlet". Default as None.
verbose (bool, optional) – Whether to print partition process. Default as True.
seed (int, optional) – Random seed. Default as None.
transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version.
target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

preprocess(balance=True, partition='iid', unbalance_sgm=0, num_shards=None, dir_alpha=None, verbose=True, seed=None, download=True)#

Perform FL partition on the dataset, and save each subset for each client into data{cid}.pkl file.

For details of partition schemes, please check Federated Dataset and DataPartitioner.

get_dataset(cid, type='train')#

Load subdataset for client with client ID cid from local file.

Parameters:

cid (int) – client id
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

Returns:

Dataset

get_dataloader(cid, batch_size=None, type='train')#

Return dataload for client with client ID cid.

Parameters:

cid (int) – client id
batch_size (int, optional) – batch size in DataLoader.
type (str, optional) – Dataset type, can be "train", "val" or "test". Default as "train".

class SyntheticDataset(root, path, preprocess=False)#

Bases: fedlab.contrib.dataset.basic_dataset.FedDataset

preprocess(root, path, partition=0.2)#

Preprocess the raw data to fedlab dataset format.

Parameters:

root (str) – path to the raw data.
path (str) – path to save the preprocessed datasets.
partition (float, optional) – The propotion of testset. Defaults to 0.2.

get_dataset(id, type='train')#

Get dataset class

Parameters:

id (int) – Client ID for the partial dataset to achieve.
type (str, optional) – Type of dataset, can be chosen from ["train", "val", "test"]. Defaults as "train".

Raises:

NotImplementedError –

get_dataloader(id, batch_size, type='train')#: Get data loader

core#

client#

manager#

Module Contents#

`ClientManager`	Base class for ClientManager.
`PassiveClientManager`	Passive communication `NetworkManager` for client in synchronous FL pattern.
`ActiveClientManager`	Active communication `NetworkManager` for client in asynchronous FL pattern.

class ClientManager(network: fedlab.core.network.DistNetwork, trainer: fedlab.core.model_maintainer.ModelMaintainer)#

Bases: fedlab.core.network_manager.NetworkManager

Base class for ClientManager.

ClientManager defines client activation in different communication stages.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
trainer (ModelMaintainer) – Subclass of ClientTrainer or SerialClientTrainer. Provides local_process() and uplink_package. Define local client training procedure.

setup()#

Initialization stage.

ClientManager reports number of clients simulated by current client process.

class PassiveClientManager(network: fedlab.core.network.DistNetwork, trainer: fedlab.core.model_maintainer.ModelMaintainer, logger: fedlab.utils.Logger = None)#

Bases: ClientManager

Passive communication NetworkManager for client in synchronous FL pattern.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
trainer (ModelMaintainer) – Subclass of ClientTrainer or SerialClientTrainer. Provides local_process() and uplink_package. Define local client training procedure.
logger (Logger, optional) – Object of Logger.

main_loop()#

Actions to perform when receiving a new message, including local training.

Main procedure of each client:

client waits for data from server (PASSIVELY).
after receiving data, client start local model training procedure.
client synchronizes with server actively.

synchronize()#: Synchronize with server.

class ActiveClientManager(network: fedlab.core.network.DistNetwork, trainer: fedlab.core.client.trainer.ClientTrainer, logger: fedlab.utils.Logger = None)#

Bases: ClientManager

Active communication NetworkManager for client in asynchronous FL pattern.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
trainer (ClientTrainer) – Subclass of ClientTrainer. Provides local_process() and uplink_package. Define local client training procedure.
logger (Logger, optional) – Object of Logger.

main_loop()#

Actions to perform on receiving new message, including local training.

client requests data from server (ACTIVELY).
after receiving data, client will train local model.
client will synchronize with server actively.

request()#: Client request.

synchronize()#: Synchronize with server.

trainer#

Module Contents#

`ClientTrainer`	An abstract class representing a client trainer.
`SerialClientTrainer`	Base class. Simulate multiple clients in sequence in a single process.

class ClientTrainer(model: torch.nn.Module, cuda: bool, device: str = None)#

Bases: fedlab.core.model_maintainer.ModelMaintainer

An abstract class representing a client trainer.

In FedLab, we define the backend of client trainer show manage its local model. It should have a function to update its model called local_process().

If you use our framework to define the activities of client, please make sure that your self-defined class should subclass it. All subclasses should overwrite local_process() and property uplink_package.

Parameters:

model (torch.nn.Module) – PyTorch model.
cuda (bool) – Use GPUs or not.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.

abstract property uplink_package: List[torch.Tensor]#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

abstract setup_dataset()#: Set up local dataset self.dataset for clients.

abstract setup_optim()#: Set up variables for optimization algorithms.

abstract classmethod local_process(payload: List[torch.Tensor])#

Manager of the upper layer will call this function with accepted payload

In synchronous mode, return True to end current FL round.

abstract train()#: Override this method to define the training procedure. This function should manipulate self._model.

abstract validate()#: Validate quality of local model.

abstract evaluate()#: Evaluate quality of local model.

class SerialClientTrainer(model: torch.nn.Module, num_clients: int, cuda: bool, device: str = None, personal: bool = False)#

Bases: fedlab.core.model_maintainer.SerialModelMaintainer

Base class. Simulate multiple clients in sequence in a single process.

Parameters:

model (torch.nn.Module) – Model used in this federation.
num_clients (int) – Number of clients in current trainer.
cuda (bool) – Use GPUs or not. Default: False.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

abstract property uplink_package: List[List[torch.Tensor]]#

Return a tensor list for uploading to server.

This attribute will be called by client manager. Customize it for new algorithms.

abstract setup_dataset()#: Override this function to set up local dataset for clients

abstract setup_optim()#

abstract classmethod local_process(id_list: list, payload: List[torch.Tensor])#: Define the local main process.

abstract train()#: Override this method to define the algorithm of training your model. This function should manipulate self._model

abstract evaluate()#: Evaluate quality of local model.

abstract validate()#: Validate quality of local model.

Package Contents#

`ClientManager`	Base class for ClientManager.
`ActiveClientManager`	Active communication `NetworkManager` for client in asynchronous FL pattern.
`PassiveClientManager`	Passive communication `NetworkManager` for client in synchronous FL pattern.

`ORDINARY_TRAINER`
`SERIAL_TRAINER`

ORDINARY_TRAINER = 0#

SERIAL_TRAINER = 1#

class ClientManager(network: fedlab.core.network.DistNetwork, trainer: fedlab.core.model_maintainer.ModelMaintainer)#

Bases: fedlab.core.network_manager.NetworkManager

Base class for ClientManager.

ClientManager defines client activation in different communication stages.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
trainer (ModelMaintainer) – Subclass of ClientTrainer or SerialClientTrainer. Provides local_process() and uplink_package. Define local client training procedure.

setup()#

Initialization stage.

ClientManager reports number of clients simulated by current client process.

class ActiveClientManager(network: fedlab.core.network.DistNetwork, trainer: fedlab.core.client.trainer.ClientTrainer, logger: fedlab.utils.Logger = None)#

Bases: ClientManager

Active communication NetworkManager for client in asynchronous FL pattern.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
trainer (ClientTrainer) – Subclass of ClientTrainer. Provides local_process() and uplink_package. Define local client training procedure.
logger (Logger, optional) – Object of Logger.

main_loop()#

Actions to perform on receiving new message, including local training.

client requests data from server (ACTIVELY).
after receiving data, client will train local model.
client will synchronize with server actively.

request()#: Client request.

synchronize()#: Synchronize with server.

class PassiveClientManager(network: fedlab.core.network.DistNetwork, trainer: fedlab.core.model_maintainer.ModelMaintainer, logger: fedlab.utils.Logger = None)#

Bases: ClientManager

Passive communication NetworkManager for client in synchronous FL pattern.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
trainer (ModelMaintainer) – Subclass of ClientTrainer or SerialClientTrainer. Provides local_process() and uplink_package. Define local client training procedure.
logger (Logger, optional) – Object of Logger.

main_loop()#

Actions to perform when receiving a new message, including local training.

Main procedure of each client:

client waits for data from server (PASSIVELY).
after receiving data, client start local model training procedure.
client synchronizes with server actively.

synchronize()#: Synchronize with server.

communicator#

FedLab communication API

package#

Module Contents#

Package

A basic network package data structure used in FedLab. Everything is Tensor in FedLab.

supported_torch_dtypes

supported_torch_dtypes#

class Package(message_code: fedlab.utils.message_code.MessageCode = None, content: List[torch.Tensor] = None)#

Bases: object

A basic network package data structure used in FedLab. Everything is Tensor in FedLab.

Note

slice_size_i = tensor_i.shape[0], that is, every element in slices indicates the size of a sub-Tensor in content.

Package maintains 3 variables:

header : torch.Tensor([sender_rank, recv_rank, content_size, message_code, data_type])
slices : list[slice_size_1, slice_size_2]
content : torch.Tensor([tensor_1, tensor_2, ...])

Parameters:

message_code (MessageCode) – Message code
content (torch.Tensor, optional) – Tensors contained in this package.

append_tensor(tensor: torch.Tensor)#

Append new tensor to Package.content

Parameters:: tensor (torch.Tensor) – Tensor to append in content.

append_tensor_list(tensor_list: List[torch.Tensor])#

Append a list of tensors to Package.content.

Parameters:: tensor_list (list[torch.Tensor]) – A list of tensors to append to Package.content.

to(dtype)#

static parse_content(slices, content)#

Parse package content into a list of tensors

Parameters:

slices (list[int]) – A list containing number of elements of each tensor. Each number is used as offset in parsing process.
content (torch.Tensor) – Package.content, a 1-D tensor composed of several 1-D tensors and their corresponding offsets. For more details about Package.

Returns:

A list of 1-D tensors parsed from content

Return type:

list[torch.Tensor]

static parse_header(header)#

Parse header to get information of current package.

Parameters:: header (torch.Tensor) – Package.header, a 1-D tensor composed of 4 elements: torch.Tensor([sender_rank, recv_rank, slice_size, message_code, data_type]).

:param For more details about Package.:

Returns:: A tuple containing 5 elements: (sender_rank, recv_rank, slice_size, message_code, data_type).
Return type:: tuple

processor#

Module Contents#

PackageProcessor

Provide more flexible distributed tensor communication functions based on

class PackageProcessor#

Bases: object

Provide more flexible distributed tensor communication functions based on torch.distributed.send() and torch.distributed.recv().

PackageProcessor defines the details of point-to-point package communication.

EVERYTHING is torch.Tensor in FedLab.

static send_package(package, dst)#

Three-segment tensor communication pattern based on torch.distributed

Pattern is shown as follows:

1.1 sender: send a header tensor containing slice_size to receiver

1.2 receiver: receive the header, and get the value of slice_size and create a buffer for incoming slices of content

2.1 sender: send a list of slices indicating the size of every content size.

2.2 receiver: receive the slices list.

3.1 sender: send a content tensor composed of a list of tensors.

3.2 receiver: receive the content tensor, and parse it to obtain slices list using parser function

static recv_package(src=None)#

Three-segment tensor communication pattern based on torch.distributed

Pattern is shown as follows:

1.1 sender: send a header tensor containing slice_size to receiver

1.2 receiver: receive the header, and get the value of slice_size and create a buffer for incoming slices of content

2.1 sender: send a list of slices indicating the size of every content size.

2.2 receiver: receive the slices list.

3.1 sender: send a content tensor composed of a list of tensors.

3.2 receiver: receive the content tensor, and parse it to obtain slices list using parser function

Package Contents#

`dtype_torch2flab`(torch_type)
`dtype_flab2torch`(fedlab_type)

`HEADER_SENDER_RANK_IDX`
`HEADER_RECEIVER_RANK_IDX`
`HEADER_SLICE_SIZE_IDX`
`HEADER_MESSAGE_CODE_IDX`
`HEADER_DATA_TYPE_IDX`
`DEFAULT_RECEIVER_RANK`
`DEFAULT_SLICE_SIZE`
`DEFAULT_MESSAGE_CODE_VALUE`
`HEADER_SIZE`
`INT8`
`INT16`
`INT32`
`INT64`
`FLOAT16`
`FLOAT32`
`FLOAT64`

HEADER_SENDER_RANK_IDX = 0#

HEADER_RECEIVER_RANK_IDX = 1#

HEADER_SLICE_SIZE_IDX = 2#

HEADER_MESSAGE_CODE_IDX = 3#

HEADER_DATA_TYPE_IDX = 4#

DEFAULT_RECEIVER_RANK#

DEFAULT_SLICE_SIZE = 0#

DEFAULT_MESSAGE_CODE_VALUE = 0#

HEADER_SIZE = 5#

INT8 = 0#

INT16 = 1#

INT32 = 2#

INT64 = 3#

FLOAT16 = 4#

FLOAT32 = 5#

FLOAT64 = 6#

dtype_torch2flab(torch_type)#

dtype_flab2torch(fedlab_type)#

server#

hierarchical#

connector#

Module Contents#

`Connector`	Abstract class for basic Connector, which is a sub-module of `Scheduler`.
`ServerConnector`	Connect with server.
`ClientConnector`	Connect with clients.

class Connector(network: fedlab.core.network.DistNetwork, write_queue: torch.multiprocessing.Queue, read_queue: torch.multiprocessing.Queue)#

Bases: fedlab.core.network_manager.NetworkManager

Abstract class for basic Connector, which is a sub-module of Scheduler.

Connector inherits NetworkManager, maintaining two Message Queue. One is for sending messages to collaborator, the other is for read messages from others.

Note

Connector is a basic component for scheduler, Example code can be seen in scheduler.py.

Parameters:

network (DistNetwork) – Manage torch.distributed network communication.
write_queue (torch.multiprocessing.Queue) – Message queue to write.
read_queue (torch.multiprocessing.Queue) – Message queue to read.

abstract process_meessage_queue()#: Define the procedure of dealing with message queue.

class ServerConnector(network: fedlab.core.network.DistNetwork, write_queue: torch.multiprocessing.Queue, read_queue: torch.multiprocessing.Queue, logger: fedlab.utils.Logger = None)#

Bases: Connector

Connect with server.

this process will act like a client.

This class is a part of middle server which used in hierarchical structure.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
write_queue (torch.multiprocessing.Queue) – Message queue to write.
read_queue (torch.multiprocessing.Queue) – Message queue to read.
logger (Logger, optional) – object of Logger. Defaults to None.

run()#

Main Process:

Initialization stage.

FL communication stage.

Shutdown stage. Close network connection.

setup()#

Initialize network connection and necessary setups.

At first, self._network.init_network_connection() is required to be called.

Overwrite this method to implement system setup message communication procedure.

main_loop()#: Define the actions of communication stage.

process_meessage_queue()#: client -> server directly transport.

class ClientConnector(network: fedlab.core.network.DistNetwork, write_queue: torch.multiprocessing.Queue, read_queue: torch.multiprocessing.Queue, logger: fedlab.utils.Logger = None)#

Bases: Connector

Connect with clients.

This class is a part of middle server which used in hierarchical structure.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
write_queue (torch.multiprocessing.Queue) – Message queue to write.
read_queue (torch.multiprocessing.Queue) – Message queue to read.
logger (Logger, optional) – object of Logger. Defaults to None.

run()#

Main Process:

Initialization stage.

FL communication stage.

Shutdown stage. Close network connection.

setup()#

Initialize network connection and necessary setups.

At first, self._network.init_network_connection() is required to be called.

Overwrite this method to implement system setup message communication procedure.

main_loop()#: Define the actions of communication stage.

process_meessage_queue()#

Process message queue

Strategy of processing message from server.

scheduler#

Module Contents#

Scheduler

Middle Topology for hierarchical communication pattern.

class Scheduler(net_upper: fedlab.core.network.DistNetwork, net_lower: fedlab.core.network.DistNetwork)#

Middle Topology for hierarchical communication pattern.

Scheduler uses message queues to decouple connector modules.

Parameters:

net_upper (DistNetwork) – Distributed network manager of server from upper level.
net_lower (DistNetwork) – Distributed network manager of clients from lower level.

run()#

Package Contents#

`ClientConnector`	Connect with clients.
`ServerConnector`	Connect with server.
`Scheduler`	Middle Topology for hierarchical communication pattern.

class ClientConnector(network: fedlab.core.network.DistNetwork, write_queue: torch.multiprocessing.Queue, read_queue: torch.multiprocessing.Queue, logger: fedlab.utils.Logger = None)#

Bases: Connector

Connect with clients.

This class is a part of middle server which used in hierarchical structure.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
write_queue (torch.multiprocessing.Queue) – Message queue to write.
read_queue (torch.multiprocessing.Queue) – Message queue to read.
logger (Logger, optional) – object of Logger. Defaults to None.

run()#

Main Process:

Initialization stage.

FL communication stage.

Shutdown stage. Close network connection.

setup()#

Initialize network connection and necessary setups.

At first, self._network.init_network_connection() is required to be called.

Overwrite this method to implement system setup message communication procedure.

main_loop()#: Define the actions of communication stage.

process_meessage_queue()#

Process message queue

Strategy of processing message from server.

class ServerConnector(network: fedlab.core.network.DistNetwork, write_queue: torch.multiprocessing.Queue, read_queue: torch.multiprocessing.Queue, logger: fedlab.utils.Logger = None)#

Bases: Connector

Connect with server.

this process will act like a client.

This class is a part of middle server which used in hierarchical structure.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
write_queue (torch.multiprocessing.Queue) – Message queue to write.
read_queue (torch.multiprocessing.Queue) – Message queue to read.
logger (Logger, optional) – object of Logger. Defaults to None.

run()#

Main Process:

Initialization stage.

FL communication stage.

Shutdown stage. Close network connection.

setup()#

Initialize network connection and necessary setups.

At first, self._network.init_network_connection() is required to be called.

Overwrite this method to implement system setup message communication procedure.

main_loop()#: Define the actions of communication stage.

process_meessage_queue()#: client -> server directly transport.

class Scheduler(net_upper: fedlab.core.network.DistNetwork, net_lower: fedlab.core.network.DistNetwork)#

Middle Topology for hierarchical communication pattern.

Scheduler uses message queues to decouple connector modules.

Parameters:

net_upper (DistNetwork) – Distributed network manager of server from upper level.
net_lower (DistNetwork) – Distributed network manager of clients from lower level.

run()#

handler#

Module Contents#

ServerHandler

An abstract class representing handler of parameter server.

class ServerHandler(model: torch.nn.Module, cuda: bool, device: str = None)#

Bases: fedlab.core.model_maintainer.ModelMaintainer

An abstract class representing handler of parameter server.

Please make sure that your self-defined server handler class subclasses this class

Example

Read source code of SyncServerHandler and AsyncServerHandler.

Parameters:

model (torch.nn.Module) – PyTorch model.
cuda (bool) – Use GPUs or not.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.

abstract property downlink_package: List[torch.Tensor]#: Property for manager layer. Server manager will call this property when activates clients.

abstract property if_stop: bool#: NetworkManager keeps monitoring this attribute, and it will stop all related processes and threads when True returned.

abstract setup_optim()#: Override this function to load your optimization hyperparameters.

abstract global_update(buffer)#

abstract load(payload)#: Override this function to define how to update global model (aggregation or optimization).

abstract evaluate()#: Override this function to define the evaluation of global model.

manager#

Module Contents#

`ServerManager`	Base class of ServerManager.
`SynchronousServerManager`	Synchronous communication
`AsynchronousServerManager`	Asynchronous communication network manager for server

DEFAULT_SERVER_RANK

DEFAULT_SERVER_RANK = 0#

class ServerManager(network: fedlab.core.network.DistNetwork, handler: fedlab.core.server.handler.ServerHandler, mode: str = 'LOCAL')#

Bases: fedlab.core.network_manager.NetworkManager

Base class of ServerManager.

Parameters:

network (DistNetwork) – Network configuration and interfaces.
handler (ServerHandler) – Performe global model update procedure.

setup()#

Initialization Stage.

Server accept local client num report from client manager.
Init a coordinator for client_id -> rank mapping.

class SynchronousServerManager(network: fedlab.core.network.DistNetwork, handler: fedlab.core.server.handler.ServerHandler, mode: str = 'LOCAL', logger: fedlab.utils.Logger = None)#

Bases: ServerManager

Synchronous communication

This is the top class in our framework which is mainly responsible for network communication of SERVER!. Synchronously communicate with clients following agreements defined in main_loop().

Parameters:

network (DistNetwork) – Network configuration and interfaces.
handler (ServerHandler) – Backend calculation handler for parameter server.
logger (Logger, optional) – Object of Logger.

main_loop()#

Actions to perform in server when receiving a package from one client.

Server transmits received package to backend computation handler for aggregation or others manipulations.

Loop:

activate clients for current training round.
listen for message from clients -> transmit received parameters to server handler.

Note

Communication agreements related: user can overwrite this function to customize communication agreements. This method is key component connecting behaviors of ServerHandler and NetworkManager.

Raises:: Exception – Unexpected MessageCode.

shutdown()#: Shutdown stage.

activate_clients()#

Activate subset of clients to join in one FL round

Manager will start a new thread to send activation package to chosen clients’ process rank. The id of clients are obtained from handler.sample_clients(). And their communication ranks are are obtained via coordinator.

shutdown_clients()#

Shutdown all clients.

Send package to each client with MessageCode.Exit.

Note

Communication agreements related: User can overwrite this function to define package for exiting information.

class AsynchronousServerManager(network: fedlab.core.network.DistNetwork, handler: fedlab.core.server.handler.ServerHandler, logger: fedlab.utils.Logger = None)#

Bases: ServerManager

Asynchronous communication network manager for server

This is the top class in our framework which is mainly responsible for network communication of SERVER!. Asynchronously communicate with clients following agreements defined in mail_loop().

Parameters:

network (DistNetwork) – Network configuration and interfaces.
handler (ServerHandler) – Backend computation handler for parameter server.
logger (Logger, optional) – Object of Logger.

main_loop()#

Communication agreements of asynchronous FL.

Server receive ParameterRequest from client. Send model parameter to client.
Server receive ParameterUpdate from client. Transmit parameters to queue waiting for aggregation.

Raises:: ValueError – invalid message code.

shutdown()#

Shutdown stage.

Close the network connection in the end.

updater_thread()#: Asynchronous communication maintain a message queue. A new thread will be started to keep monitoring message queue.

shutdown_clients()#

Shutdown all clients.

Send package to clients with MessageCode.Exit.

Package Contents#

`SynchronousServerManager`	Synchronous communication
`AsynchronousServerManager`	Asynchronous communication network manager for server

class SynchronousServerManager(network: fedlab.core.network.DistNetwork, handler: fedlab.core.server.handler.ServerHandler, mode: str = 'LOCAL', logger: fedlab.utils.Logger = None)#

Bases: ServerManager

Synchronous communication

This is the top class in our framework which is mainly responsible for network communication of SERVER!. Synchronously communicate with clients following agreements defined in main_loop().

Parameters:

network (DistNetwork) – Network configuration and interfaces.
handler (ServerHandler) – Backend calculation handler for parameter server.
logger (Logger, optional) – Object of Logger.

main_loop()#

Actions to perform in server when receiving a package from one client.

Server transmits received package to backend computation handler for aggregation or others manipulations.

Loop:

activate clients for current training round.
listen for message from clients -> transmit received parameters to server handler.

Note

Communication agreements related: user can overwrite this function to customize communication agreements. This method is key component connecting behaviors of ServerHandler and NetworkManager.

Raises:: Exception – Unexpected MessageCode.

shutdown()#: Shutdown stage.

activate_clients()#

Activate subset of clients to join in one FL round

shutdown_clients()#

Shutdown all clients.

Send package to each client with MessageCode.Exit.

Note

Communication agreements related: User can overwrite this function to define package for exiting information.

class AsynchronousServerManager(network: fedlab.core.network.DistNetwork, handler: fedlab.core.server.handler.ServerHandler, logger: fedlab.utils.Logger = None)#

Bases: ServerManager

Asynchronous communication network manager for server

This is the top class in our framework which is mainly responsible for network communication of SERVER!. Asynchronously communicate with clients following agreements defined in mail_loop().

Parameters:

network (DistNetwork) – Network configuration and interfaces.
handler (ServerHandler) – Backend computation handler for parameter server.
logger (Logger, optional) – Object of Logger.

main_loop()#

Communication agreements of asynchronous FL.

Server receive ParameterRequest from client. Send model parameter to client.
Server receive ParameterUpdate from client. Transmit parameters to queue waiting for aggregation.

Raises:: ValueError – invalid message code.

shutdown()#

Shutdown stage.

Close the network connection in the end.

updater_thread()#: Asynchronous communication maintain a message queue. A new thread will be started to keep monitoring message queue.

shutdown_clients()#

Shutdown all clients.

Send package to clients with MessageCode.Exit.

coordinator#

Module Contents#

Coordinator

Deal with the mapping relation between client id and process rank in FL system.

class Coordinator(setup_dict: dict, mode: str = 'LOCAL')#

Bases: object

Deal with the mapping relation between client id and process rank in FL system.

Note: Server Manager creates a Coordinator following: 1. init network connection. 2. client send local group info (the number of client simulating in local) to server. 4. server receive all info and init a server Coordinator.

Parameters:

setup_dict (dict) – A dict like {rank:client_num …}, representing the map relation between process rank and client id.
mode (str, optional) – “GLOBAL” and “LOCAL”. Coordinator will map client id to (rank, global id) or (rank, local id) according to mode. For example, client id 51 is in a machine which has 1 manager and serial trainer simulating 10 clients. LOCAL id means the index of its 10 clients. Therefore, global id 51 will be mapped into local id 1 (depending on setting).

property total#

map_id(id)#

a map function from client id to (rank,local id)

Parameters:: id (int) – client id
Returns:: rank in distributed group and local id.
Return type:: rank, id

map_id_list(id_list: list)#

a map function from id_list to dict{rank:local id}

This can be very useful in Scale modules.

Parameters:: id_list (list(int)) – a list of client id.
Returns:: contains process rank and its relative local client ids.
Return type:: map_dict (dict)

switch()#

__str__() → str#: Return str(self).

__call__(info)#

model_maintainer#

Module Contents#

`ModelMaintainer`	Maintain PyTorch model.
`SerialModelMaintainer`	"Maintain PyTorch model.

class ModelMaintainer(model: torch.nn.Module, cuda: bool, device: str = None)#

Bases: object

Maintain PyTorch model.

Provide necessary attributes and operation methods. More features with local or global model will be implemented here.

Parameters:

model (torch.nn.Module) – PyTorch model.
cuda (bool) – Use GPUs or not.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest memory as default.

property model: torch.nn.Module#: Return torch.nn.module.

property model_parameters: torch.Tensor#: Return serialized model parameters.

property model_gradients: torch.Tensor#: Return serialized model gradients.

property shape_list: List[torch.Tensor]#

Return shape of model parameters.

Currently, this attributes used in tensor compression.

set_model(parameters: torch.Tensor)#: Assign parameters to self._model.

class SerialModelMaintainer(model: torch.nn.Module, num_clients: int, cuda: bool, device: str = None, personal: bool = False)#

Bases: ModelMaintainer

“Maintain PyTorch model.

Provide necessary attributes and operation methods. More features with local or global model will be implemented here.

Parameters:

model (torch.nn.Module) – PyTorch model.
num_clients (int) – The number of independent models.
cuda (bool) – Use GPUs or not.
device (str, optional) – Assign model/data to the given GPUs. E.g., ‘device:0’ or ‘device:0,1’. Defaults to None. If device is None and cuda is True, FedLab will set the gpu with the largest idle memory as default.
personal (bool, optional) – If Ture is passed, SerialModelMaintainer will generate the copy of local parameters list and maintain them respectively. These paremeters are indexed by [0, num-1]. Defaults to False.

set_model(parameters: torch.Tensor = None, id: int = None)#

Assign parameters to self._model.

Note

parameters and id can not be None at the same time. If id is None, this function load the given parameters. If id is not None, this function load the parameters of given id first and the parameters attribute will be ignored.

Parameters:

parameters (torch.Tensor, optional) – Model parameters. Defaults to None.
id (int, optional) – Load the model parameters of client id. Defaults to None.

network#

Module Contents#

DistNetwork

Manage torch.distributed network.

type2byte

type2byte#

class DistNetwork(address: tuple, world_size: int, rank: int, ethernet: str = None, dist_backend: str = 'gloo')#

Bases: object

Manage torch.distributed network.

Parameters:

address (tuple) – Address of this server in form of (SERVER_ADDR, SERVER_IP)
world_size (int) – the size of this distributed group (including server).
rank (int) – the rank of process in distributed group.
ethernet (str) – the name of local ethernet. User could check it using command ifconfig.
dist_backend (str or torch.distributed.Backend) – backend of torch.distributed. Valid values include mpi, gloo, and nccl. Default: gloo.

init_network_connection()#: Initialize torch.distributed communication group

close_network_connection()#: Destroy current torch.distributed process group

send(content=None, message_code=None, dst=0, count=True)#: Send tensor to process rank=dst

recv(src=None, count=True)#: Receive tensor from process rank=src

broadcast_send(content=None, message_code=None, dst=None, count=True)#

broadcast_recv(src=None, count=True)#

__str__()#: Return str(self).

network_manager#

Module Contents#

NetworkManager

Abstract class.

class NetworkManager(network: fedlab.core.network.DistNetwork)#

Bases: torch.multiprocessing.Process

Abstract class.

Parameters:: network (DistNetwork) – object to manage torch.distributed network communication.

run()#

Main Process:

Initialization stage.

FL communication stage.

Shutdown stage. Close network connection.

setup()#

Initialize network connection and necessary setups.

At first, self._network.init_network_connection() is required to be called.

Overwrite this method to implement system setup message communication procedure.

abstract main_loop()#: Define the actions of communication stage.

shutdown()#

Shutdown stage.

Close the network connection in the end.

standalone#

Module Contents#

StandalonePipeline

class StandalonePipeline(handler: fedlab.core.server.handler.ServerHandler, trainer: fedlab.core.client.trainer.SerialClientTrainer)#

Bases: object

main()#

evaluate()#

Package Contents#

`DistNetwork`	Manage `torch.distributed` network.
`NetworkManager`	Abstract class.

class DistNetwork(address: tuple, world_size: int, rank: int, ethernet: str = None, dist_backend: str = 'gloo')#

Bases: object

Manage torch.distributed network.

Parameters:

address (tuple) – Address of this server in form of (SERVER_ADDR, SERVER_IP)
world_size (int) – the size of this distributed group (including server).
rank (int) – the rank of process in distributed group.
ethernet (str) – the name of local ethernet. User could check it using command ifconfig.
dist_backend (str or torch.distributed.Backend) – backend of torch.distributed. Valid values include mpi, gloo, and nccl. Default: gloo.

init_network_connection()#: Initialize torch.distributed communication group

close_network_connection()#: Destroy current torch.distributed process group

send(content=None, message_code=None, dst=0, count=True)#: Send tensor to process rank=dst

recv(src=None, count=True)#: Receive tensor from process rank=src

broadcast_send(content=None, message_code=None, dst=None, count=True)#

broadcast_recv(src=None, count=True)#

__str__()#: Return str(self).

class NetworkManager(network: fedlab.core.network.DistNetwork)#

Bases: torch.multiprocessing.Process

Abstract class.

Parameters:: network (DistNetwork) – object to manage torch.distributed network communication.

run()#

Main Process:

Initialization stage.

FL communication stage.

Shutdown stage. Close network connection.

setup()#

Initialize network connection and necessary setups.

At first, self._network.init_network_connection() is required to be called.

Overwrite this method to implement system setup message communication procedure.

abstract main_loop()#: Define the actions of communication stage.

shutdown()#

Shutdown stage.

Close the network connection in the end.

models#

cnn#

CNN model in pytorch .. rubric:: References

[1] Reddi S, Charles Z, Zaheer M, et al. Adaptive Federated Optimization. ICML 2020. https://arxiv.org/pdf/2003.00295.pdf

Module Contents#

`CNN_FEMNIST`	Used for EMNIST experiments in references[1]
`CNN_MNIST`
`CNN_CIFAR10`	from torch tutorial
`AlexNet_CIFAR10`

class CNN_FEMNIST(only_digits=False)#

Bases: torch.nn.Module

Used for EMNIST experiments in references[1] :param only_digits: If True, uses a final layer with 10 outputs, for use with the

digits only MNIST dataset (http://yann.lecun.com/exdb/mnist/). If selfalse, uses 62 outputs for selfederated Extended MNIST (selfEMNIST) EMNIST: Extending MNIST to handwritten letters: https://arxiv.org/abs/1702.05373 Defaluts to True

Returns:: A torch.nn.Module.

forward(x)#

class CNN_MNIST#

Bases: torch.nn.Module

forward(x)#

class CNN_CIFAR10#

Bases: torch.nn.Module

from torch tutorial https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html

forward(x)#

class AlexNet_CIFAR10(num_classes=10)#

Bases: torch.nn.Module

forward(x)#

mlp#

Module Contents#

`MLP_CelebA`	Used for celeba experiment
`MLP`

class MLP_CelebA#

Bases: torch.nn.Module

Used for celeba experiment

forward(x)#

class MLP(input_size, output_size)#

Bases: torch.nn.Module

forward(x)#

rnn#

RNN model in pytorch .. rubric:: References

[1] H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agueray Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. AISTATS 2017. https://arxiv.org/abs/1602.05629 [2] Reddi S, Charles Z, Zaheer M, et al. Adaptive Federated Optimization. ICML 2020. https://arxiv.org/pdf/2003.00295.pdf

Module Contents#

`RNN_Shakespeare`
`LSTMModel`

class RNN_Shakespeare(vocab_size=80, embedding_dim=8, hidden_size=256)#

Bases: torch.nn.Module

forward(input_seq)#

class LSTMModel(vocab_size, embedding_dim, hidden_size, num_layers, output_dim, pad_idx=0, using_pretrained=False, embedding_weights=None, bid=False)#

Bases: torch.nn.Module

forward(input_seq: torch.Tensor)#

Package Contents#

`CNN_CIFAR10`	from torch tutorial
`CNN_FEMNIST`	Used for EMNIST experiments in references[1]
`CNN_MNIST`
`RNN_Shakespeare`
`MLP`
`MLP_CelebA`	Used for celeba experiment

class CNN_CIFAR10#

Bases: torch.nn.Module

from torch tutorial https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html

forward(x)#

class CNN_FEMNIST(only_digits=False)#

Bases: torch.nn.Module

Used for EMNIST experiments in references[1] :param only_digits: If True, uses a final layer with 10 outputs, for use with the

digits only MNIST dataset (http://yann.lecun.com/exdb/mnist/). If selfalse, uses 62 outputs for selfederated Extended MNIST (selfEMNIST) EMNIST: Extending MNIST to handwritten letters: https://arxiv.org/abs/1702.05373 Defaluts to True

Returns:: A torch.nn.Module.

forward(x)#

class CNN_MNIST#

Bases: torch.nn.Module

forward(x)#

class RNN_Shakespeare(vocab_size=80, embedding_dim=8, hidden_size=256)#

Bases: torch.nn.Module

forward(input_seq)#

class MLP(input_size, output_size)#

Bases: torch.nn.Module

forward(x)#

class MLP_CelebA#

Bases: torch.nn.Module

Used for celeba experiment

forward(x)#

utils#

dataset#

functional#

Module Contents#

`split_indices`(num_cumsum, rand_perm)	Splice the sample index list given number of each client.
`balance_split`(num_clients, num_samples)	Assign same sample sample for each client.
`lognormal_unbalance_split`(num_clients, num_samples, ...)	Assign different sample number for each client using Log-Normal distribution.
`dirichlet_unbalance_split`(num_clients, num_samples, alpha)	Assign different sample number for each client using Dirichlet distribution.
`homo_partition`(client_sample_nums, num_samples)	Partition data indices in IID way given sample numbers for each clients.
`hetero_dir_partition`(targets, num_clients, ...[, ...])	Non-iid partition based on Dirichlet distribution. The method is from "hetero-dir" partition of
`shards_partition`(targets, num_clients, num_shards)	Non-iid partition used in FedAvg paper.
`client_inner_dirichlet_partition`(targets, num_clients, ...)	Non-iid Dirichlet partition.
`label_skew_quantity_based_partition`(targets, ...)	Label-skew:quantity-based partition.
`fcube_synthetic_partition`(data)	Feature-distribution-skew:synthetic partition.
`samples_num_count`(client_dict, num_clients)	Return sample count for all clients in `client_dict`.
`noniid_slicing`(dataset, num_clients, num_shards)	Slice a dataset for non-IID.
`random_slicing`(dataset, num_clients)	Slice a dataset randomly and equally for IID.

split_indices(num_cumsum, rand_perm)#

Splice the sample index list given number of each client.

Parameters:

num_cumsum (np.ndarray) – Cumulative sum of sample number for each client.
rand_perm (list) – List of random sample index.

Returns:

{ client_id: indices}.

Return type:

dict

balance_split(num_clients, num_samples)#

Assign same sample sample for each client.

Parameters:

num_clients (int) – Number of clients for partition.
num_samples (int) – Total number of samples.

Returns:

A numpy array consisting num_clients integer elements, each represents sample number of corresponding clients.

Return type:

numpy.ndarray

lognormal_unbalance_split(num_clients, num_samples, unbalance_sgm)#

Assign different sample number for each client using Log-Normal distribution.

Sample numbers for clients are drawn from Log-Normal distribution.

Parameters:

num_clients (int) – Number of clients for partition.
num_samples (int) – Total number of samples.
unbalance_sgm (float) – Log-normal variance. When equals to 0, the partition is equal to balance_partition().

Returns:

A numpy array consisting num_clients integer elements, each represents sample number of corresponding clients.

Return type:

numpy.ndarray

dirichlet_unbalance_split(num_clients, num_samples, alpha)#

Assign different sample number for each client using Dirichlet distribution.

Sample numbers for clients are drawn from Dirichlet distribution.

Parameters:

num_clients (int) – Number of clients for partition.
num_samples (int) – Total number of samples.
alpha (float) – Dirichlet concentration parameter

Returns:

A numpy array consisting num_clients integer elements, each represents sample number of corresponding clients.

Return type:

numpy.ndarray

homo_partition(client_sample_nums, num_samples)#

Partition data indices in IID way given sample numbers for each clients.

Parameters:

client_sample_nums (numpy.ndarray) – Sample numbers for each clients.
num_samples (int) – Number of samples.

Returns:

{ client_id: indices}.

Return type:

dict

hetero_dir_partition(targets, num_clients, num_classes, dir_alpha, min_require_size=None)#

Non-iid partition based on Dirichlet distribution. The method is from “hetero-dir” partition of Bayesian Nonparametric Federated Learning of Neural Networks and Federated Learning with Matched Averaging.

This method simulates heterogeneous partition for which number of data points and class proportions are unbalanced. Samples will be partitioned into $J$ clients by sampling $p_k \sim \text{Dir}_{J}({\alpha})$ and allocating a $p_{p,j}$ proportion of the samples of class $k$ to local client $j$.

Sample number for each client is decided in this function.

Parameters:

targets (list or numpy.ndarray) – Sample targets. Unshuffled preferred.
num_clients (int) – Number of clients for partition.
num_classes (int) – Number of classes in samples.
dir_alpha (float) – Parameter alpha for Dirichlet distribution.
min_require_size (int, optional) – Minimum required sample number for each client. If set to None, then equals to num_classes.

Returns:

{ client_id: indices}.

Return type:

dict

shards_partition(targets, num_clients, num_shards)#

Non-iid partition used in FedAvg paper.

Parameters:

targets (list or numpy.ndarray) – Sample targets. Unshuffled preferred.
num_clients (int) – Number of clients for partition.
num_shards (int) – Number of shards in partition.

Returns:

{ client_id: indices}.

Return type:

dict

client_inner_dirichlet_partition(targets, num_clients, num_classes, dir_alpha, client_sample_nums, verbose=True)#

Non-iid Dirichlet partition.

The method is from The method is from paper Federated Learning Based on Dynamic Regularization. This function can be used by given specific sample number for all clients client_sample_nums. It’s different from hetero_dir_partition().

Parameters:

targets (list or numpy.ndarray) – Sample targets.
num_clients (int) – Number of clients for partition.
num_classes (int) – Number of classes in samples.
dir_alpha (float) – Parameter alpha for Dirichlet distribution.
client_sample_nums (numpy.ndarray) – A numpy array consisting num_clients integer elements, each represents sample number of corresponding clients.
verbose (bool, optional) – Whether to print partition process. Default as True.

Returns:

{ client_id: indices}.

Return type:

dict

label_skew_quantity_based_partition(targets, num_clients, num_classes, major_classes_num)#

Label-skew:quantity-based partition.

For details, please check Federated Learning on Non-IID Data Silos: An Experimental Study.

Parameters:

targets (List or np.ndarray) – Labels od dataset.
num_clients (int) – Number of clients.
num_classes (int) – Number of unique classes.
major_classes_num (int) – Number of classes for each client, should be less then num_classes.

Returns:

{ client_id: indices}.

Return type:

dict

fcube_synthetic_partition(data)#

Feature-distribution-skew:synthetic partition.

Synthetic partition for FCUBE dataset. This partition is from Federated Learning on Non-IID Data Silos: An Experimental Study.

Parameters:: data (np.ndarray) – Data of dataset FCUBE.
Returns:: { client_id: indices}.
Return type:: dict

samples_num_count(client_dict, num_clients)#

Return sample count for all clients in client_dict.

Parameters:

client_dict (dict) – Data partition result for different clients.
num_clients (int) – Total number of clients.

Returns:

pandas.DataFrame

noniid_slicing(dataset, num_clients, num_shards)#

Slice a dataset for non-IID.

Parameters:

dataset (torch.utils.data.Dataset) – Dataset to slice.
num_clients (int) – Number of client.
num_shards (int) – Number of shards.

Notes

The size of a shard equals to int(len(dataset)/num_shards). Each client will get int(num_shards/num_clients) shards.

Returns：: dict: { 0: indices of dataset, 1: indices of dataset, ..., k: indices of dataset }

random_slicing(dataset, num_clients)#

Slice a dataset randomly and equally for IID.

Args：: dataset (torch.utils.data.Dataset): a dataset for slicing. num_clients (int): the number of client.
Returns：: dict: { 0: indices of dataset, 1: indices of dataset, ..., k: indices of dataset }

partition#

Module Contents#

`DataPartitioner`	Base class for data partition in federated learning.
`CIFAR10Partitioner`	CIFAR10 data partitioner.
`CIFAR100Partitioner`	CIFAR100 data partitioner.
`BasicPartitioner`	Basic data partitioner.
`VisionPartitioner`	Data partitioner for vision data.
`MNISTPartitioner`	Data partitioner for MNIST.
`FMNISTPartitioner`	Data partitioner for FashionMNIST.
`SVHNPartitioner`	Data partitioner for SVHN.
`FCUBEPartitioner`	FCUBE data partitioner.
`AdultPartitioner`	Data partitioner for Adult.
`RCV1Partitioner`	Data partitioner for RCV1.
`CovtypePartitioner`	Data partitioner for Covtype.

class DataPartitioner#

Bases: abc.ABC

Base class for data partition in federated learning.

Examples of DataPartitioner: BasicPartitioner, CIFAR10Partitioner.

Details and tutorials of different data partition and datasets, please check Federated Dataset and DataPartitioner.

abstract _perform_partition()#

abstract __getitem__(index)#

abstract __len__()#

class CIFAR10Partitioner(targets, num_clients, balance=True, partition='iid', unbalance_sgm=0, num_shards=None, dir_alpha=None, verbose=True, min_require_size=None, seed=None)#

Bases: DataPartitioner

CIFAR10 data partitioner.

Partition CIFAR10 given specific client number. Currently 6 supported partition schemes can be achieved by passing different combination of parameters in initialization:

balance=None
- partition="dirichlet": non-iid partition used in Bayesian Nonparametric Federated Learning of Neural Networks and Federated Learning with Matched Averaging. Refer to fedlab.utils.dataset.functional.hetero_dir_partition() for more information.
- partition="shards": non-iid method used in FedAvg paper. Refer to fedlab.utils.dataset.functional.shards_partition() for more information.
balance=True: “Balance” refers to FL scenario that sample numbers for different clients are the same. Refer to fedlab.utils.dataset.functional.balance_partition() for more information.
- partition="iid": Random select samples from complete dataset given sample number for each client.
- partition="dirichlet": Refer to fedlab.utils.dataset.functional.client_inner_dirichlet_partition() for more information.
balance=False: “Unbalance” refers to FL scenario that sample numbers for different clients are different. For unbalance method, sample number for each client is drown from Log-Normal distribution with variance unbalanced_sgm. When unbalanced_sgm=0, partition is balanced. Refer to fedlab.utils.dataset.functional.lognormal_unbalance_partition() for more information. The method is from paper Federated Learning Based on Dynamic Regularization.
- partition="iid": Random select samples from complete dataset given sample number for each client.
- partition="dirichlet": Refer to fedlab.utils.dataset.functional.client_inner_dirichlet_partition() for more information.

For detail usage, please check Federated Dataset and DataPartitioner.

Parameters:

targets (list or numpy.ndarray) – Targets of dataset for partition. Each element is in range of [0, 1, …, 9].
num_clients (int) – Number of clients for data partition.
balance (bool, optional) – Balanced partition over all clients or not. Default as True.
partition (str, optional) – Partition type, only "iid", shards, "dirichlet" are supported. Default as "iid".
unbalance_sgm (float, optional) – Log-normal distribution variance for unbalanced data partition over clients. Default as 0 for balanced partition.
num_shards (int, optional) – Number of shards in non-iid "shards" partition. Only works if partition="shards". Default as None.
dir_alpha (float, optional) – Dirichlet distribution parameter for non-iid partition. Only works if partition="dirichlet". Default as None.
verbose (bool, optional) – Whether to print partition process. Default as True.
min_require_size (int, optional) – Minimum required sample number for each client. If set to None, then equals to num_classes. Only works if partition="noniid-labeldir".
seed (int, optional) – Random seed. Default as None.

num_classes = 10#

_perform_partition()#

__getitem__(index)#

Obtain sample indices for client index.

Parameters:: index (int) – Client ID.
Returns:: List of sample indices for client ID index.
Return type:: list

__len__()#: Usually equals to number of clients.

class CIFAR100Partitioner(targets, num_clients, balance=True, partition='iid', unbalance_sgm=0, num_shards=None, dir_alpha=None, verbose=True, min_require_size=None, seed=None)#

Bases: CIFAR10Partitioner

CIFAR100 data partitioner.

This is a subclass of the CIFAR10Partitioner. For details, please check Federated Dataset and DataPartitioner.

num_classes = 100#

class BasicPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, min_require_size=None, seed=None)#

Bases: DataPartitioner

Basic data partitioner.

Basic data partitioner, supported partition:

label-distribution-skew:quantity-based
label-distribution-skew:distributed-based (Dirichlet)
quantity-skew (Dirichlet)
IID

For more details, please check Federated Learning on Non-IID Data Silos: An Experimental Study and Federated Dataset and DataPartitioner.

Parameters:

targets (list or numpy.ndarray) – Sample targets. Unshuffled preferred.
num_clients (int) – Number of clients for partition.
partition (str) – Partition name. Only supports "noniid-#label", "noniid-labeldir", "unbalance" and "iid" partition schemes.
dir_alpha (float) – Parameter alpha for Dirichlet distribution. Only works if partition="noniid-labeldir".
major_classes_num (int) – Number of major class for each clients. Only works if partition="noniid-#label".
verbose (bool) – Whether output intermediate information. Default as True.
min_require_size (int, optional) – Minimum required sample number for each client. If set to None, then equals to num_classes. Only works if partition="noniid-labeldir".
seed (int) – Random seed. Default as None.

Returns:

{ client_id: indices}.

Return type:

dict

num_classes = 2#

_perform_partition()#

__getitem__(index)#

__len__()#

class VisionPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)#

Bases: BasicPartitioner

Data partitioner for vision data.

Supported partition for vision data:

label-distribution-skew:quantity-based
label-distribution-skew:distributed-based (Dirichlet)
quantity-skew (Dirichlet)
IID

For more details, please check Federated Learning on Non-IID Data Silos: An Experimental Study.

Parameters:

targets (list or numpy.ndarray) – Sample targets. Unshuffled preferred.
num_clients (int) – Number of clients for partition.
partition (str) – Partition name. Only supports "noniid-#label", "noniid-labeldir", "unbalance" and "iid" partition schemes.
dir_alpha (float) – Parameter alpha for Dirichlet distribution. Only works if partition="noniid-labeldir".
major_classes_num (int) – Number of major class for each clients. Only works if partition="noniid-#label".
verbose (bool) – Whether output intermediate information. Default as True.
seed (int) – Random seed. Default as None.

Returns:

{ client_id: indices}.

Return type:

dict

num_classes = 10#

class MNISTPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)#

Bases: VisionPartitioner

Data partitioner for MNIST.

For details, please check VisionPartitioner and Federated Dataset and DataPartitioner.

num_features = 784#

class FMNISTPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)#

Bases: VisionPartitioner

Data partitioner for FashionMNIST.

For details, please check VisionPartitioner and Federated Dataset and DataPartitioner

num_features = 784#

class SVHNPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)#

Bases: VisionPartitioner

Data partitioner for SVHN.

For details, please check VisionPartitioner and Federated Dataset and DataPartitioner

num_features = 1024#

class FCUBEPartitioner(data, partition)#

Bases: DataPartitioner

FCUBE data partitioner.

FCUBE is a synthetic dataset for research in non-IID scenario with feature imbalance. This dataset and its partition methods are proposed in Federated Learning on Non-IID Data Silos: An Experimental Study.

Supported partition methods for FCUBE:

feature-distribution-skew:synthetic
IID

For more details, please refer to Section (IV-B-b) of original paper. For detailed usage, please check Federated Dataset and DataPartitioner.

Parameters:

data (numpy.ndarray) – Data of dataset FCUBE.
partition (str) – Partition type. Only supports ‘synthetic’ and ‘iid’.

num_classes = 2#

num_clients = 4#

_perform_partition()#

__getitem__(index)#

__len__()#

class AdultPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, min_require_size=None, seed=None)#

Bases: BasicPartitioner

Data partitioner for Adult.

For details, please check BasicPartitioner and Federated Dataset and DataPartitioner

num_features = 123#

num_classes = 2#

class RCV1Partitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, min_require_size=None, seed=None)#

Bases: BasicPartitioner

Data partitioner for RCV1.

For details, please check BasicPartitioner and Federated Dataset and DataPartitioner

num_features = 47236#

num_classes = 2#

class CovtypePartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, min_require_size=None, seed=None)#

Bases: BasicPartitioner

Data partitioner for Covtype.

For details, please check BasicPartitioner and Federated Dataset and DataPartitioner

num_features = 54#

num_classes = 2#

Package Contents#

`DataPartitioner`	Base class for data partition in federated learning.
`BasicPartitioner`	Basic data partitioner.
`VisionPartitioner`	Data partitioner for vision data.
`CIFAR10Partitioner`	CIFAR10 data partitioner.
`CIFAR100Partitioner`	CIFAR100 data partitioner.
`FMNISTPartitioner`	Data partitioner for FashionMNIST.
`MNISTPartitioner`	Data partitioner for MNIST.
`SVHNPartitioner`	Data partitioner for SVHN.
`FCUBEPartitioner`	FCUBE data partitioner.
`AdultPartitioner`	Data partitioner for Adult.
`RCV1Partitioner`	Data partitioner for RCV1.
`CovtypePartitioner`	Data partitioner for Covtype.

class DataPartitioner#

Bases: abc.ABC

Base class for data partition in federated learning.

Examples of DataPartitioner: BasicPartitioner, CIFAR10Partitioner.

Details and tutorials of different data partition and datasets, please check Federated Dataset and DataPartitioner.

abstract _perform_partition()#

abstract __getitem__(index)#

abstract __len__()#

class BasicPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, min_require_size=None, seed=None)#

Bases: DataPartitioner

Basic data partitioner.

Basic data partitioner, supported partition:

label-distribution-skew:quantity-based
label-distribution-skew:distributed-based (Dirichlet)
quantity-skew (Dirichlet)
IID

For more details, please check Federated Learning on Non-IID Data Silos: An Experimental Study and Federated Dataset and DataPartitioner.

Parameters:

targets (list or numpy.ndarray) – Sample targets. Unshuffled preferred.
num_clients (int) – Number of clients for partition.
partition (str) – Partition name. Only supports "noniid-#label", "noniid-labeldir", "unbalance" and "iid" partition schemes.
dir_alpha (float) – Parameter alpha for Dirichlet distribution. Only works if partition="noniid-labeldir".
major_classes_num (int) – Number of major class for each clients. Only works if partition="noniid-#label".
verbose (bool) – Whether output intermediate information. Default as True.
min_require_size (int, optional) – Minimum required sample number for each client. If set to None, then equals to num_classes. Only works if partition="noniid-labeldir".
seed (int) – Random seed. Default as None.

Returns:

{ client_id: indices}.

Return type:

dict

num_classes = 2#

_perform_partition()#

__getitem__(index)#

__len__()#

class VisionPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)#

Bases: BasicPartitioner

Data partitioner for vision data.

Supported partition for vision data:

label-distribution-skew:quantity-based
label-distribution-skew:distributed-based (Dirichlet)
quantity-skew (Dirichlet)
IID

For more details, please check Federated Learning on Non-IID Data Silos: An Experimental Study.

Parameters:

targets (list or numpy.ndarray) – Sample targets. Unshuffled preferred.
num_clients (int) – Number of clients for partition.
partition (str) – Partition name. Only supports "noniid-#label", "noniid-labeldir", "unbalance" and "iid" partition schemes.
dir_alpha (float) – Parameter alpha for Dirichlet distribution. Only works if partition="noniid-labeldir".
major_classes_num (int) – Number of major class for each clients. Only works if partition="noniid-#label".
verbose (bool) – Whether output intermediate information. Default as True.
seed (int) – Random seed. Default as None.

Returns:

{ client_id: indices}.

Return type:

dict

num_classes = 10#

Bases: DataPartitioner

CIFAR10 data partitioner.

Partition CIFAR10 given specific client number. Currently 6 supported partition schemes can be achieved by passing different combination of parameters in initialization:

balance=None
- partition="dirichlet": non-iid partition used in Bayesian Nonparametric Federated Learning of Neural Networks and Federated Learning with Matched Averaging. Refer to fedlab.utils.dataset.functional.hetero_dir_partition() for more information.
- partition="shards": non-iid method used in FedAvg paper. Refer to fedlab.utils.dataset.functional.shards_partition() for more information.
balance=True: “Balance” refers to FL scenario that sample numbers for different clients are the same. Refer to fedlab.utils.dataset.functional.balance_partition() for more information.
- partition="iid": Random select samples from complete dataset given sample number for each client.
- partition="dirichlet": Refer to fedlab.utils.dataset.functional.client_inner_dirichlet_partition() for more information.
balance=False: “Unbalance” refers to FL scenario that sample numbers for different clients are different. For unbalance method, sample number for each client is drown from Log-Normal distribution with variance unbalanced_sgm. When unbalanced_sgm=0, partition is balanced. Refer to fedlab.utils.dataset.functional.lognormal_unbalance_partition() for more information. The method is from paper Federated Learning Based on Dynamic Regularization.
- partition="iid": Random select samples from complete dataset given sample number for each client.
- partition="dirichlet": Refer to fedlab.utils.dataset.functional.client_inner_dirichlet_partition() for more information.

For detail usage, please check Federated Dataset and DataPartitioner.

Parameters:

targets (list or numpy.ndarray) – Targets of dataset for partition. Each element is in range of [0, 1, …, 9].
num_clients (int) – Number of clients for data partition.
balance (bool, optional) – Balanced partition over all clients or not. Default as True.
partition (str, optional) – Partition type, only "iid", shards, "dirichlet" are supported. Default as "iid".
unbalance_sgm (float, optional) – Log-normal distribution variance for unbalanced data partition over clients. Default as 0 for balanced partition.
num_shards (int, optional) – Number of shards in non-iid "shards" partition. Only works if partition="shards". Default as None.
dir_alpha (float, optional) – Dirichlet distribution parameter for non-iid partition. Only works if partition="dirichlet". Default as None.
verbose (bool, optional) – Whether to print partition process. Default as True.
min_require_size (int, optional) – Minimum required sample number for each client. If set to None, then equals to num_classes. Only works if partition="noniid-labeldir".
seed (int, optional) – Random seed. Default as None.

num_classes = 10#

_perform_partition()#

__getitem__(index)#

Obtain sample indices for client index.

Parameters:: index (int) – Client ID.
Returns:: List of sample indices for client ID index.
Return type:: list

__len__()#: Usually equals to number of clients.

Bases: CIFAR10Partitioner

CIFAR100 data partitioner.

This is a subclass of the CIFAR10Partitioner. For details, please check Federated Dataset and DataPartitioner.

num_classes = 100#

class FMNISTPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)#

Bases: VisionPartitioner

Data partitioner for FashionMNIST.

For details, please check VisionPartitioner and Federated Dataset and DataPartitioner

num_features = 784#

class MNISTPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)#

Bases: VisionPartitioner

Data partitioner for MNIST.

For details, please check VisionPartitioner and Federated Dataset and DataPartitioner.

num_features = 784#

class SVHNPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)#

Bases: VisionPartitioner

Data partitioner for SVHN.

For details, please check VisionPartitioner and Federated Dataset and DataPartitioner

num_features = 1024#

class FCUBEPartitioner(data, partition)#

Bases: DataPartitioner

FCUBE data partitioner.

Supported partition methods for FCUBE:

feature-distribution-skew:synthetic
IID

For more details, please refer to Section (IV-B-b) of original paper. For detailed usage, please check Federated Dataset and DataPartitioner.

Parameters:

data (numpy.ndarray) – Data of dataset FCUBE.
partition (str) – Partition type. Only supports ‘synthetic’ and ‘iid’.

num_classes = 2#

num_clients = 4#

_perform_partition()#

__getitem__(index)#

__len__()#

class AdultPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, min_require_size=None, seed=None)#

Bases: BasicPartitioner

Data partitioner for Adult.

For details, please check BasicPartitioner and Federated Dataset and DataPartitioner

num_features = 123#

num_classes = 2#

class RCV1Partitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, min_require_size=None, seed=None)#

Bases: BasicPartitioner

Data partitioner for RCV1.

For details, please check BasicPartitioner and Federated Dataset and DataPartitioner

num_features = 47236#

num_classes = 2#

class CovtypePartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, min_require_size=None, seed=None)#

Bases: BasicPartitioner

Data partitioner for Covtype.

For details, please check BasicPartitioner and Federated Dataset and DataPartitioner

num_features = 54#

num_classes = 2#

aggregator#

Module Contents#

Aggregators

Define the algorithm of parameters aggregation

class Aggregators#

Bases: object

Define the algorithm of parameters aggregation

static fedavg_aggregate(serialized_params_list, weights=None)#

FedAvg aggregator

Paper: http://proceedings.mlr.press/v54/mcmahan17a.html

Parameters:

serialized_params_list (list[torch.Tensor])) – Merge all tensors following FedAvg.
weights (list, numpy.array or torch.Tensor, optional) – Weights for each params, the length of weights need to be same as length of serialized_params_list

Returns:

torch.Tensor

static fedasync_aggregate(server_param, new_param, alpha)#

FedAsync aggregator

Paper: https://arxiv.org/abs/1903.03934

functional#

Module Contents#

AverageMeter

Record metrics information

`setup_seed`(seed)
`evaluate`(model, criterion, test_loader)	Evaluate classify task model accuracy.
`read_config_from_json`(json_file, user_name)	Read config from json_file to get config for user_name
`get_best_gpu`()	Return gpu (`torch.device`) with largest free memory.
`partition_report`(targets, data_indices[, class_num, ...])	Generate data partition report for clients in `data_indices`.

setup_seed(seed)#

class AverageMeter#

Bases: object

Record metrics information

reset()#

update(val, n=1)#

evaluate(model, criterion, test_loader)#

Evaluate classify task model accuracy.

Returns:: (loss.sum, acc.avg)

read_config_from_json(json_file: str, user_name: str)#

Read config from json_file to get config for user_name

Parameters:

json_file (str) – path for json_file
user_name (str) – read config for this user, it can be ‘server’ or ‘client_id’

Returns:

a tuple with ip, port, world_size, rank about user with user_name

Examples

read_config_from_json(‘../../../tests/data/config.json’, ‘server’)

Notes

config.json example as follows {

“server”: {
“ip” : “127.0.0.1”, “port”: “3002”, “world_size”: 3, “rank”: 0

}, “client_0”: {

“ip”: “127.0.0.1”, “port”: “3002”, “world_size”: 3, “rank”: 1

}, “client_1”: {

“ip”: “127.0.0.1”, “port”: “3002”, “world_size”: 3, “rank”: 2

}

}

get_best_gpu()#: Return gpu (torch.device) with largest free memory.

partition_report(targets, data_indices, class_num=None, verbose=True, file=None)#

Generate data partition report for clients in data_indices.

Generate data partition report for each client according to data_indices, including ratio of each class and dataset size in current client. Report can be printed in screen or into file. The output format is comma-separated values which can be read by pandas.read_csv() or csv.reader().

Parameters:

targets (list or numpy.ndarray) – Targets for all data samples, with each element is in range of 0 to class_num-1.
data_indices (dict) – Dict of client_id: [data indices].
class_num (int, optional) – Total number of classes. If set to None, then class_num = max(targets) + 1.
verbose (bool, optional) – Whether print data partition report in screen. Default as True.
file (str, optional) – Output file name of data partition report. If None, then no output in file. Default as None.

Examples

First generate synthetic data labels and data partition to obtain data_indices ({ client_id: sample indices}):

>>> sample_num = 15
>>> class_num = 4
>>> clients_num = 3
>>> num_per_client = int(sample_num/clients_num)
>>> labels = np.random.randint(class_num, size=sample_num)  # generate 15 labels, each label is 0 to 3
>>> rand_per = np.random.permutation(sample_num)
>>> # partition synthetic data into 3 clients
>>> data_indices = {0: rand_per[0:num_per_client],
...                 1: rand_per[num_per_client:num_per_client*2],
...                 2: rand_per[num_per_client*2:num_per_client*3]}

Check data_indices may look like:

>>> data_indices
{0: array([8, 6, 5, 7, 2]),
 1: array([ 3, 10, 14,  4,  1]),
 2: array([13,  9, 12, 11,  0])}

Now generate partition report for each client and each class:

>>> partition_report(labels, data_indices, class_num=class_num, verbose=True, file=None)
Class frequencies:
client,class0,class1,class2,class3,Amount
Client   0,0.200,0.00,0.200,0.600,5
Client   1,0.400,0.200,0.200,0.200,5
Client   2,0.00,0.400,0.400,0.200,5

logger#

Module Contents#

Logger

record cmd info to file and print it to cmd at the same time

class Logger(log_name=None, log_file=None)#

Bases: object

record cmd info to file and print it to cmd at the same time

Parameters:

log_name (str) – log name for output.
log_file (str) – a file path of log file.

info(log_str)#: Print information to logger

warning(warning_str)#: Print warning to logger

message_code#

Module Contents#

MessageCode

Different types of messages between client and server that we support go here.

class MessageCode#

Bases: enum.Enum

Different types of messages between client and server that we support go here.

ParameterRequest = 0#

GradientUpdate = 1#

ParameterUpdate = 2#

EvaluateParams = 3#

Exit = 4#

SetUp = 5#

Activation = 6#

serialization#

Module Contents#

SerializationTool

class SerializationTool#

Bases: object

static serialize_model_gradients(model: torch.nn.Module) → torch.Tensor#

_summary_

Parameters:: model (torch.nn.Module) – _description_
Returns:: _description_
Return type:: torch.Tensor

static deserialize_model_gradients(model: torch.nn.Module, gradients: torch.Tensor)#

static serialize_model(model: torch.nn.Module) → torch.Tensor#

Unfold model parameters

Unfold every layer of model, concate all of tensors into one. Return a torch.Tensor with shape (size, ).

Please note that we update the implementation. Current version of serialization includes the parameters in batchnorm layers.

Parameters:: model (torch.nn.Module) – model to serialize.

static deserialize_model(model: torch.nn.Module, serialized_parameters: torch.Tensor, mode='copy')#

Assigns serialized parameters to model.parameters. This is done by iterating through model.parameters() and assigning the relevant params in grad_update. NOTE: this function manipulates model.parameters.

Parameters:

model (torch.nn.Module) – model to deserialize.
serialized_parameters (torch.Tensor) – serialized model parameters.
mode (str) – deserialize mode. “copy” or “add”.

static serialize_trainable_model(model: torch.nn.Module) → torch.Tensor#

Unfold model parameters

Unfold every layer of model, concate all of tensors into one. Return a torch.Tensor with shape (size, ).

Parameters:: model (torch.nn.Module) – model to serialize.

static deserialize_trainable_model(model: torch.nn.Module, serialized_parameters: torch.Tensor, mode='copy')#

Parameters:

model (torch.nn.Module) – model to deserialize.
serialized_parameters (torch.Tensor) – serialized model parameters.
mode (str) – deserialize mode. “copy” or “add”.

Package Contents#

`Aggregators`	Define the algorithm of parameters aggregation
`Logger`	record cmd info to file and print it to cmd at the same time
`MessageCode`	Different types of messages between client and server that we support go here.
`SerializationTool`

class Aggregators#

Bases: object

Define the algorithm of parameters aggregation

static fedavg_aggregate(serialized_params_list, weights=None)#

FedAvg aggregator

Paper: http://proceedings.mlr.press/v54/mcmahan17a.html

Parameters:

serialized_params_list (list[torch.Tensor])) – Merge all tensors following FedAvg.
weights (list, numpy.array or torch.Tensor, optional) – Weights for each params, the length of weights need to be same as length of serialized_params_list

Returns:

torch.Tensor

static fedasync_aggregate(server_param, new_param, alpha)#

FedAsync aggregator

Paper: https://arxiv.org/abs/1903.03934

class Logger(log_name=None, log_file=None)#

Bases: object

record cmd info to file and print it to cmd at the same time

Parameters:

log_name (str) – log name for output.
log_file (str) – a file path of log file.

info(log_str)#: Print information to logger

warning(warning_str)#: Print warning to logger

class MessageCode#

Bases: enum.Enum

Different types of messages between client and server that we support go here.

ParameterRequest = 0#

GradientUpdate = 1#

ParameterUpdate = 2#

EvaluateParams = 3#

Exit = 4#

SetUp = 5#

Activation = 6#

class SerializationTool#

Bases: object

static serialize_model_gradients(model: torch.nn.Module) → torch.Tensor#

_summary_

Parameters:: model (torch.nn.Module) – _description_
Returns:: _description_
Return type:: torch.Tensor

static deserialize_model_gradients(model: torch.nn.Module, gradients: torch.Tensor)#

static serialize_model(model: torch.nn.Module) → torch.Tensor#

Unfold model parameters

Unfold every layer of model, concate all of tensors into one. Return a torch.Tensor with shape (size, ).

Please note that we update the implementation. Current version of serialization includes the parameters in batchnorm layers.

Parameters:: model (torch.nn.Module) – model to serialize.

static deserialize_model(model: torch.nn.Module, serialized_parameters: torch.Tensor, mode='copy')#

Parameters:

model (torch.nn.Module) – model to deserialize.
serialized_parameters (torch.Tensor) – serialized model parameters.
mode (str) – deserialize mode. “copy” or “add”.

static serialize_trainable_model(model: torch.nn.Module) → torch.Tensor#

Unfold model parameters

Unfold every layer of model, concate all of tensors into one. Return a torch.Tensor with shape (size, ).

Parameters:: model (torch.nn.Module) – model to serialize.

static deserialize_trainable_model(model: torch.nn.Module, serialized_parameters: torch.Tensor, mode='copy')#

Parameters:

model (torch.nn.Module) – model to deserialize.
serialized_parameters (torch.Tensor) – serialized model parameters.
mode (str) – deserialize mode. “copy” or “add”.

Package Contents#

__version__ = '1.3.0'#