trainer#

Module Contents#

`ClientTrainer`	An abstract class representing a client trainer.
`SGDClientTrainer`	Client backend handler, this class provides data process method to upper layer.

class ClientTrainer(model, cuda)#

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.

property 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.

abstract classmethod local_process(self, payload) → bool#

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

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

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

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

class SGDClientTrainer(model, data_loader, epochs, optimizer, criterion, cuda=False, logger=None)#

Bases: ClientTrainer

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

Parameters

model (torch.nn.Module) – PyTorch model.
data_loader (torch.utils.data.DataLoader) – torch.utils.data.DataLoader for this client.
epochs (int) – the number of local epoch.
optimizer (torch.optim.Optimizer) – optimizer for this client’s model.
criterion (torch.nn.Loss) – loss function used in local training process.
cuda (bool, optional) – use GPUs or not. Default: False.
logger (Logger, optional) – :object of Logger.

property uplink_package(self)#

Return a tensor list for uploading to server.

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

local_process(self, payload)#

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

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

train(self, model_parameters) → None#

Client trains its local model on local dataset.

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