FedXgbNnAvg#

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

Configurable FedXgbNnAvg strategy implementation.

Avertissement

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.