FaultTolerantFedAvg#
- class FaultTolerantFedAvg(*, fraction_fit: float = 1.0, fraction_evaluate: float = 1.0, min_fit_clients: int = 1, min_evaluate_clients: int = 1, min_available_clients: int = 1, evaluate_fn: Callable[[int, list[numpy.ndarray[Any, numpy.dtype[Any]]], dict[str, Union[bool, bytes, float, int, str]]], tuple[float, dict[str, Union[bool, bytes, float, int, str]]] | None] | None = None, on_fit_config_fn: Callable[[int], dict[str, Union[bool, bytes, float, int, str]]] | None = None, on_evaluate_config_fn: Callable[[int], dict[str, Union[bool, bytes, float, int, str]]] | None = None, min_completion_rate_fit: float = 0.5, min_completion_rate_evaluate: float = 0.5, initial_parameters: Parameters | None = None, fit_metrics_aggregation_fn: Callable[[list[tuple[int, dict[str, Union[bool, bytes, float, int, str]]]]], dict[str, Union[bool, bytes, float, int, str]]] | None = None, evaluate_metrics_aggregation_fn: Callable[[list[tuple[int, dict[str, Union[bool, bytes, float, int, str]]]]], dict[str, Union[bool, bytes, float, int, str]]] | None = None)[소스]#
기반 클래스:
FedAvg
Configurable fault-tolerant FedAvg strategy implementation.
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[flwr.server.client_proxy.ClientProxy, flwr.common.typing.EvaluateRes]], failures: list[Union[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.EvaluateRes], BaseException]]) tuple[Optional[float], dict[str, Union[bool, bytes, float, int, str]]] [소스]#
Aggregate evaluation losses using weighted average.
- aggregate_fit(server_round: int, results: list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.FitRes]], failures: list[Union[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.FitRes], BaseException]]) tuple[Optional[flwr.common.typing.Parameters], dict[str, Union[bool, bytes, float, int, str]]] [소스]#
Aggregate fit results using weighted average.
- configure_evaluate(server_round: int, parameters: Parameters, client_manager: ClientManager) list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.EvaluateIns]] #
Configure the next round of evaluation.
- configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.FitIns]] #
Configure the next round of training.
- evaluate(server_round: int, parameters: Parameters) tuple[float, dict[str, Union[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.