fl4health.servers.adaptive_constraint_servers.mrmtl_server module

class MrMtlServer(client_manager, fl_config, strategy, reporters=None, checkpoint_and_state_module=None, on_init_parameters_config_fn=None, server_name=None, accept_failures=True)[source]

Bases: FlServer

__init__(client_manager, fl_config, strategy, reporters=None, checkpoint_and_state_module=None, on_init_parameters_config_fn=None, server_name=None, accept_failures=True)[source]

This is a very basic wrapper class over the FlServer to ensure that the strategy used for MR-MTL is of type FedAvgWithAdaptiveConstraint.

Parameters:

  • client_manager (ClientManager) – Determines the mechanism by which clients are sampled by the server, if they are to be sampled at all.

  • fl_config (Config) – This should be the configuration that was used to setup the federated training. In most cases it should be the “source of truth” for how FL training/evaluation should proceed. For example, the config used to produce the on_fit_config_fn and on_evaluate_config_fn for the strategy. NOTE: This config is DISTINCT from the Flwr server config, which is extremely minimal.

  • strategy (FedAvgWithAdaptiveConstraint) – The aggregation strategy to be used by the server to handle. client updates and other information potentially sent by the participating clients. For MR-MTL, the strategy must be a derivative of the FedAvgWithAdaptiveConstraint class.

  • reporters (Sequence[BaseReporter], optional) – A sequence of FL4Health reporters which the server should send data to before and after each round. Defaults to None.

  • checkpoint_and_state_module (AdaptiveConstraintServerCheckpointAndStateModule | None, optional) – This module is used to handle both model checkpointing and state checkpointing. The former is aimed at saving model artifacts to be used or evaluated after training. The latter is used to preserve training state (including models) such that if FL training is interrupted, the process may be restarted. If no module is provided, no checkpointing or state preservation will happen. Defaults to None. NOTE: For MR-MTL, the server model is an aggregation of the personal models, which isn’t the target of FL training for this algorithm. However, one may still want to save this model for other purposes.

  • on_init_parameters_config_fn (Callable[[int], dict[str, Scalar]] | None, optional) – Function used to configure how one asks a client to provide parameters from which to initialize all other clients by providing a Config dictionary. If this is none, then a blank config is sent with the parameter request (which is default behavior for flower servers). Defaults to None.

  • server_name (str | None, optional) – An optional string name to uniquely identify server. This name is also used as part of any state checkpointing done by the server. Defaults to None.

  • accept_failures (bool, optional) – Determines whether the server should accept failures during training or evaluation from clients or not. If set to False, this will cause the server to shutdown all clients and throw an exception. Defaults to True.