FedAvg

class FedAvg(*, 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 | None = None, 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, inplace: bool = True)

기반 클래스: Strategy

Federated Averaging strategy.

Implementation based on https://arxiv.org/abs/1602.05629

매개변수:

• fraction_fit (float, optional) – Fraction of clients used during training. In case min_fit_clients is larger than fraction_fit * available_clients, min_fit_clients will still be sampled. Defaults to 1.0.

• fraction_evaluate (float, optional) – Fraction of clients used during validation. In case min_evaluate_clients is larger than fraction_evaluate * available_clients, min_evaluate_clients will still be sampled. 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, optional) – Initial global model parameters.

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

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

• inplace (bool (default: True)) – Enable (True) or disable (False) in-place aggregation of model updates.

메소드

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]]

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.