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.