handler#

Module Contents#

ParameterServerBackendHandler

An abstract class representing handler of parameter server.

SyncParameterServerHandler

Synchronous Parameter Server Handler.

AsyncParameterServerHandler

Asynchronous Parameter Server Handler
class ParameterServerBackendHandler(model, cuda=False)#

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 SyncParameterServerHandler and AsyncParameterServerHandler.

abstract _update_model(self, model_parameters_list)#

Override this function for global model aggregation strategy.

Parameters

model_parameters_list (list[torch.Tensor]) – A list of serialized model parameters collected from different clients.

class SyncParameterServerHandler(model, global_round=5, cuda=False, sample_ratio=1.0, logger=Logger())#

Bases: ParameterServerBackendHandler

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 used in this federation.

  • global_round (int) – stop condition. Shut down FL system when global round is reached.

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

  • sample_ratio (float) – sample_ratio * client_num is the number of clients to join in every FL round. Default: 1.0.

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

stop_condition(self) → bool#

NetworkManager keeps monitoring the return of this method, and it will stop all related processes and threads when True returned.

sample_clients(self)#

Return a list of client rank indices selected randomly. The client ID is from 1 to self.client_num_in_total + 1.

add_model(self, sender_rank, model_parameters)#

Deal with incoming model parameters from one client.

Note

Return True when self._update_model is called.

Parameters

  • sender_rank (int) – Rank of sender client in torch.distributed group.

  • model_parameters (torch.Tensor) – Serialized model parameters from one client.

_update_model(self, model_parameters_list)#

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

model_parameters_list (list[torch.Tensor]) – A list of parameters.aq

property client_num_per_round(self)#
class AsyncParameterServerHandler(model, alpha=0.5, total_time=5, strategy='constant', cuda=False, logger=Logger())#

Bases: ParameterServerBackendHandler

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

  • alpha (float) – weight used in async aggregation.

  • total_time (int) – stop condition. Shut down FL system when total_time is reached.

  • strategy (str) – adaptive strategy. constant, hinge and polynomial is optional. Default: constant.

  • cuda (bool) – Use GPUs or not.

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

property server_time(self)#
stop_condition(self) → bool#

NetworkManager keeps monitoring the return of this method, and it will stop all related processes and threads when True returned.

_update_model(self, client_model_parameters, model_time)#

“update global model from client_model_queue

_adapt_alpha(self, receive_model_time)#

update the alpha according to staleness