FedXgbNnAvg#

class FedXgbNnAvg(*args: Any, **kwargs: Any)[source]#

Bases: FedAvg

Configurable FedXgbNnAvg strategy implementation.

Warning

This strategy is deprecated, but a copy of it is available in Flower Baselines: https://github.com/adap/flower/tree/main/baselines/hfedxgboost.

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[Any | 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: Any) 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.