FedMedian#

class FedMedian(*, 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)[source]#

Bases: FedAvg

Configurable FedMedian strategy implementation.

Methods

`aggregate_evaluate`(server_round, results, ...)	Aggregate evaluation losses using weighted average.
`aggregate_fit`(server_round, results, failures)	Aggregate fit results using median.
`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 median.

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.