trainer

Module Contents

ClientTrainer	An abstract class representing a client backend trainer.
ClientSGDTrainer	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 backend trainer.

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

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 train().

Parameters

• model (torch.nn.Module) – PyTorch model.

• cuda (bool) – Use GPUs or not.

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 ClientSGDTrainer(model, data_loader, epochs, optimizer, criterion, cuda=False, logger=Logger())

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, optional) – optimizer for this client’s model.

• criterion (torch.nn.Loss, optional) – loss function used in local training process.

• cuda (bool, optional) – use GPUs or not. Default: False.

• logger (Logger, optional) – :object of Logger.

train(self, model_parameters) → None

Client trains its local model on local dataset.

Parameters

model_parameters (torch.Tensor) – Serialized model parameters.