AvgLogits#

class FedDF(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]#

Ensemble Distillation for Robust Model Fusion in Federated Learning.

For further details regarding the algorithm we refer to Ensemble Distillation for Robust Model Fusion in Federated Learning.

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

  • global_train_split (str) -- the name of train split to be used on server

  • global_epochs (int) -- number of training epochs on the server

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.

Warning

this algorithm needs a split for training on the server. This means that the global datasets provided in data manager should include an extra split.

init(*args, **kwrag)[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