FedAdam#

class FedAdam(*, fraction_fit: float = 1.0, fraction_evaluate: float = 1.0, min_fit_clients: int = 2, min_evaluate_clients: int = 2, min_available_clients: int = 2, evaluate_fn: Callable[[int, List[ndarray[Any, dtype[Any]]], Dict[str, bool | bytes | float | int | str]], Tuple[float, Dict[str, bool | bytes | float | int | str]] | None] | None = None, on_fit_config_fn: Callable[[int], Dict[str, bool | bytes | float | int | str]] | None = None, on_evaluate_config_fn: Callable[[int], Dict[str, bool | bytes | float | int | str]] | None = None, accept_failures: bool = True, initial_parameters: Parameters, fit_metrics_aggregation_fn: Callable[[List[Tuple[int, Dict[str, bool | bytes | float | int | str]]]], Dict[str, bool | bytes | float | int | str]] | None = None, evaluate_metrics_aggregation_fn: Callable[[List[Tuple[int, Dict[str, bool | bytes | float | int | str]]]], Dict[str, bool | bytes | float | int | str]] | None = None, eta: float = 0.1, eta_l: float = 0.1, beta_1: float = 0.9, beta_2: float = 0.99, tau: float = 1e-09)[source]#

Bases: FedOpt

FedAdam - Adaptive Federated Optimization using Adam.

Implementation based on https://arxiv.org/abs/2003.00295v5

Parameters:

  • fraction_fit (float, optional) – Fraction of clients used during training. Defaults to 1.0.

  • fraction_evaluate (float, optional) – Fraction of clients used during validation. Defaults to 1.0.

  • min_fit_clients (int, optional) – Minimum number of clients used during training. Defaults to 2.

  • min_evaluate_clients (int, optional) – Minimum number of clients used during validation. Defaults to 2.

  • min_available_clients (int, optional) – Minimum number of total clients in the system. Defaults to 2.

  • evaluate_fn (Optional[Callable[[int, NDArrays, Dict[str, Scalar]],Optional[Tuple[float, Dict[str, Scalar]]]]]) – Optional function used for validation. Defaults to None.

  • on_fit_config_fn (Callable[[int], Dict[str, Scalar]], optional) – Function used to configure training. Defaults to None.

  • on_evaluate_config_fn (Callable[[int], Dict[str, Scalar]], optional) – Function used to configure validation. Defaults to None.

  • accept_failures (bool, optional) – Whether or not accept rounds containing failures. Defaults to True.

  • initial_parameters (Parameters) – Initial global model parameters.

  • fit_metrics_aggregation_fn (Optional[MetricsAggregationFn]) – Metrics aggregation function, optional.

  • evaluate_metrics_aggregation_fn (Optional[MetricsAggregationFn]) – Metrics aggregation function, optional.

  • eta (float, optional) – Server-side learning rate. Defaults to 1e-1.

  • eta_l (float, optional) – Client-side learning rate. Defaults to 1e-1.

  • beta_1 (float, optional) – Momentum parameter. Defaults to 0.9.

  • beta_2 (float, optional) – Second moment parameter. Defaults to 0.99.

  • tau (float, optional) – Controls the algorithm’s degree of adaptability. Defaults to 1e-9.

Methods

aggregate_evaluate(server_round, results, ...)

Aggregate evaluation losses using weighted average.

aggregate_fit(server_round, results, failures)

Aggregate fit results using weighted average.

configure_evaluate(server_round, parameters, ...)

Configure the next round of evaluation.

configure_fit(server_round, parameters, ...)

Configure the next round of training.

evaluate(server_round, parameters)

Evaluate model parameters using an evaluation function.

initialize_parameters(client_manager)

Initialize global model parameters.

num_evaluation_clients(num_available_clients)

Use a fraction of available clients for evaluation.

num_fit_clients(num_available_clients)

Return the sample size and the required number of available clients.
aggregate_evaluate(server_round: int, results: List[Tuple[ClientProxy, EvaluateRes]], failures: List[Tuple[ClientProxy, EvaluateRes] | BaseException]) → Tuple[float | None, Dict[str, bool | bytes | float | int | str]]#

Aggregate evaluation losses using weighted average.

aggregate_fit(server_round: int, results: List[Tuple[ClientProxy, FitRes]], failures: List[Tuple[ClientProxy, FitRes] | BaseException]) → Tuple[Parameters | None, Dict[str, bool | bytes | float | int | str]][source]#

Aggregate fit results using weighted average.

configure_evaluate(server_round: int, parameters: Parameters, client_manager: ClientManager) → List[Tuple[ClientProxy, EvaluateIns]]#

Configure the next round of evaluation.

configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) → List[Tuple[ClientProxy, FitIns]]#

Configure the next round of training.

evaluate(server_round: int, parameters: Parameters) → Tuple[float, Dict[str, bool | bytes | float | int | str]] | None#

Evaluate model parameters using an evaluation function.

initialize_parameters(client_manager: ClientManager) → Parameters | None#

Initialize global model parameters.

num_evaluation_clients(num_available_clients: int) → Tuple[int, int]#

Use a fraction of available clients for evaluation.

num_fit_clients(num_available_clients: int) → Tuple[int, int]#

Return the sample size and the required number of available clients.