client#

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.

SerialTrainer

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

SubsetSerialTrainer

Train multiple clients in a single process.

ORDINARY_TRAINER

SERIAL_TRAINER

ORDINARY_TRAINER = 0#
SERIAL_TRAINER = 1#
class ClientManager(network, trainer)#

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 (ClientTrainer) – Subclass of ClientTrainer. Provides local_process() and uplink_package. Define local client training procedure.

setup(self)#

Initialization stage.

ClientManager reports number of clients simulated by current client process.

class ActiveClientManager(network, trainer, 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(self)#

Actions to perform on receiving new message, including local training

  1. client requests data from server (ACTIVELY).

  2. after receiving data, client will train local model.

  3. client will synchronize with server actively.

request(self)#

Client request

synchronize(self)#

Synchronize with server

class PassiveClientManager(network, trainer, logger=None)#

Bases: ClientManager

Passive communication NetworkManager for client in synchronous 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(self)#

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

Main procedure of each client:

  1. client waits for data from server (PASSIVELY).

  2. after receiving data, client start local model training procedure.

  3. client synchronizes with server actively.

synchronize(self)#

Synchronize with server

class SerialTrainer(model, client_num, cuda=False, logger=None)#

Bases: fedlab.core.client.trainer.ClientTrainer

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

Parameters

  • model (torch.nn.Module) – Model used in this federation.

  • client_num (int) – Number of clients in current trainer.

  • cuda (bool) – Use GPUs or not. Default: False.

  • logger (Logger, optional) – Object of Logger.

Return a tensor list for uploading to server.

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

abstract _train_alone(self, model_parameters, train_loader)#

Train local model with model_parameters on train_loader.

Parameters
abstract _get_dataloader(self, client_id)#

Get DataLoader for client_id.

local_process(self, id_list, payload)#

Train local model with different dataset according to client id in id_list.

Parameters

  • id_list (list[int]) – Client id in this training serial.

  • payload (list[torch.Tensor]) – communication payload from server.

class SubsetSerialTrainer(model, dataset, data_slices, logger=None, cuda=False, args={'epochs': 5, 'batch_size': 100, 'lr': 0.1})#

Bases: SerialTrainer

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.

  • dataset (torch.utils.data.Dataset) – Local dataset for this group of clients.

  • data_slices (list[list]) – Subset of indices of dataset.

  • logger (Logger, optional) – Object of Logger.

  • cuda (bool) – Use GPUs or not. Default: False.

  • args (dict) – Uncertain variables. Default: {"epochs": 5, "batch_size": 100, "lr": 0.1}

Note

len(data_slices) == client_num, that is, each sub-index of dataset corresponds to a client’s local dataset one-by-one.

_get_dataloader(self, client_id)#

Return a training dataloader used in train() for client with id

Parameters

client_id (int) – client_id of client to generate dataloader

Note

client_id here is not equal to client_id in global FL setting. It is the index of client in current SerialTrainer.

Returns

DataLoader for specific client’s sub-dataset

_train_alone(self, model_parameters, train_loader)#

Single round of local training for one client.

Note

Overwrite this method to customize the PyTorch training pipeline.

Parameters