FedXgbCyclic¶
- class FedXgbCyclic(**kwargs: Any)[source]¶
Bases:
FedAvg
Configurable FedXgbCyclic 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, ...)配置下一轮评估。
configure_fit
(server_round, parameters, ...)配置下一轮训练。
evaluate
(server_round, parameters)使用评估函数评估模型参数。
initialize_parameters
(client_manager)初始化全局模型参数。
num_evaluation_clients
(num_available_clients)使用部分可用客户进行评估。
num_fit_clients
(num_available_clients)返回样本大小和所需的可用客户数量。
- 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]] [source]¶
配置下一轮评估。
- configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) list[tuple[ClientProxy, FitIns]] [source]¶
配置下一轮训练。
- evaluate(server_round: int, parameters: Parameters) tuple[float, dict[str, bool | bytes | float | int | str]] | None ¶
使用评估函数评估模型参数。
- initialize_parameters(client_manager: ClientManager) Parameters | None ¶
初始化全局模型参数。
- num_evaluation_clients(num_available_clients: int) tuple[int, int] ¶
使用部分可用客户进行评估。
- num_fit_clients(num_available_clients: int) tuple[int, int] ¶
返回样本大小和所需的可用客户数量。