FedXgbBagging#

class FedXgbBagging(evaluate_function: Callable[[int, Parameters, Dict[str, bool | bytes | float | int | str]], Tuple[float, Dict[str, bool | bytes | float | int | str]] | None] | None = None, **kwargs: Any)[source]#

Bases : FedAvg

Configurable FedXgbBagging strategy implementation.

Methods

aggregate_evaluate(server_round, results, ...)

Aggregate evaluation metrics using average.

aggregate_fit(server_round, results, failures)

Aggregate fit results using bagging.

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]][source]#

Aggregate evaluation metrics using 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 bagging.

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[source]#

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.