FedAvg#

class FedAvg(data_manager, metric_logger, num_clients, sample_scheme, sample_rate, model_def, epochs, criterion_def, optimizer_def=functools.partial(<class 'torch.optim.sgd.SGD'>, lr=1.0), local_optimizer_def=functools.partial(<class 'torch.optim.sgd.SGD'>, lr=0.1), lr_scheduler_def=None, local_lr_scheduler_def=None, r2r_local_lr_scheduler_def=None, batch_size=32, test_batch_size=64, device='cpu', *args, **kwargs)[source]#

Implements FedAvg algorithm for centralized FL. For further details regarding the algorithm we refer to Communication-Efficient Learning of Deep Networks from Decentralized Data.

Parameters

  • data_manager (distributed.data_management.DataManager) -- data manager

  • metric_logger (logall.Logger) -- metric logger for tracking.

  • num_clients (int) -- number of clients

  • sample_scheme (str) -- mode of sampling clients. Options are 'uniform' and 'sequential'

  • sample_rate (float) -- rate of sampling clients

  • model_def (torch.Module) -- definition of for constructing the model

  • epochs (int) -- number of local epochs

  • criterion_def (Callable) -- loss function defining local objective

  • optimizer_def (Callable) -- derfintion of server optimizer

  • local_optimizer_def (Callable) -- defintoin of local optimizer

  • lr_scheduler_def (Callable) -- definition of lr scheduler of server optimizer.

  • local_lr_scheduler_def (Callable) -- definition of lr scheduler of local optimizer

  • r2r_local_lr_scheduler_def (Callable) -- definition to schedule lr that is delivered to the clients at each round (deterimined init lr of the client optimizer)

  • batch_size (int) -- batch size of the local trianing

  • test_batch_size (int) -- inference time batch size

  • device (str) -- cpu, cuda, or gpu number

Note

definition of
  • learning rate schedulers, could be any of the ones defined at

    torch.optim.lr_scheduler or any other that implements step and get_last_lr methods._schedulers``.

  • optimizers, could be any torch.optim.Optimizer.

  • model, could be any torch.Module.

  • criterion, could be any fedsim.scores.Score.

deploy()[source]#

return Mapping of name -> parameters_set to test the model

Parameters

server_storage (Storage) -- server storage object.

init()[source]#

this method is executed only once at the time of instantiating the algorithm object. Here you define your model and whatever needed during the training. Remember to write the outcome of your processing to server_storage for access in other methods.

Note

*args and **kwargs are directly passed through from algorithm constructor.

Parameters

server_storage (Storage) -- server storage object

optimize(serial_aggregator, appendix_aggregator)[source]#

optimize server mdoel(s) and return scores to be reported

Parameters

  • server_storage (Storage) -- server storage object.

  • serial_aggregator (SerialAggregator) -- serial aggregator instance of current round.

  • appendix_aggregator (AppendixAggregator) -- appendix aggregator instance of current round.

Raises

NotImplementedError -- abstract class to be implemented by child

Returns

Mapping[Hashable, Any] -- context to be reported

receive_from_client(client_id, client_msg, train_split_name, serial_aggregator, appendix_aggregator)[source]#

receive and aggregate info from selected clients

Parameters

  • server_storage (Storage) -- server storage object.

  • client_id (int) -- id of the sender (client)

  • client_msg (Mapping[Hashable, Any]) -- client context that is sent.

  • train_split_name (str) -- name of the training split on clients.

  • aggregator (SerialAggregator) -- aggregator instance to collect info.

Returns

bool -- success of the aggregation.

Raises

NotImplementedError -- abstract class to be implemented by child

report(dataloaders, rounds, scores, metric_logger, device, optimize_reports, deployment_points=None)[source]#

test on global data and report info. If a flatten dict of str:Union[int,float] is returned from this function the content is automatically logged using the metric logger (e.g., logall.TensorboardLogger). metric_logger is also passed as an input argument for extra logging operations (non scalar).

Parameters

  • server_storage (Storage) -- server storage object.

  • dataloaders (Any) -- dict of data loaders to test the global model(s)

  • round_scores (Dict[str, Dict[str, fedsim.scores.Score]]) -- dictionary of form {'split_name':{'score_name': score_def}} for global scores to evaluate at the current round.

  • metric_logger (Any, optional) -- the logging object (e.g., logall.TensorboardLogger)

  • device (str) -- 'cuda', 'cpu' or gpu number

  • optimize_reports (Mapping[Hashable, Any]) -- dict returned by optimzier

  • deployment_points (Mapping[Hashable, torch.Tensor], optional) -- output of deploy method

Raises

NotImplementedError -- abstract class to be implemented by child

send_to_client(client_id)[source]#

returns context to send to the client corresponding to client_id.

Warning

Do not send shared objects like server model if you made any before you deepcopy it.

Parameters

  • server_storage (Storage) -- server storage object.

  • client_id (int) -- id of the receiving client

Raises

NotImplementedError -- abstract class to be implemented by child

Returns

Mapping[Hashable, Any] -- the context to be sent in form of a Mapping

send_to_server(rounds, storage, datasets, train_split_name, scores, epochs, criterion, train_batch_size, inference_batch_size, optimizer_def, lr_scheduler_def=None, device='cuda', ctx=None, step_closure=None)[source]#

client operation on the recieved information.

Parameters

  • id (int) -- id of the client

  • rounds (int) -- global round number

  • storage (Storage) -- storage object of the client

  • datasets (Dict[str, Iterable]) -- this comes from Data Manager

  • train_split_name (str) -- string containing name of the training split

  • scores -- Dict[str, Dict[str, Score]]: dictionary of form {'split_name':{'score_name': Score}} for global scores to evaluate at the current round.

  • epochs (int) -- number of epochs to train

  • criterion (Score) -- citerion, should be a differentiable fedsim.scores.score

  • train_batch_size (int) -- training batch_size

  • inference_batch_size (int) -- inference batch_size

  • optimizer_def (float) -- class for constructing the local optimizer

  • lr_scheduler_def (float) -- class for constructing the local lr scheduler

  • device (Union[int, str], optional) -- Defaults to 'cuda'.

  • ctx (Optional[Dict[Hashable, Any]], optional) -- context reveived.

Returns

Mapping[str, Any] -- client context to be sent to the server