DPFedAvgAdaptive#
- class DPFedAvgAdaptive(strategy: Strategy, num_sampled_clients: int, init_clip_norm: float = 0.1, noise_multiplier: float = 1.0, server_side_noising: bool = True, clip_norm_lr: float = 0.2, clip_norm_target_quantile: float = 0.5, clip_count_stddev: float | None = None)[소스]#
기반 클래스:
DPFedAvgFixed
Wrapper for configuring a Strategy for DP with Adaptive Clipping.
경고
This class is deprecated and will be removed in a future release.
메소드
aggregate_evaluate
(server_round, results, ...)Aggregate evaluation losses using the given strategy.
aggregate_fit
(server_round, results, failures)Aggregate training results as in DPFedAvgFixed and update clip norms.
configure_evaluate
(server_round, parameters, ...)Configure the next round of evaluation using the specified strategy.
configure_fit
(server_round, parameters, ...)Configure the next round of training.
evaluate
(server_round, parameters)Evaluate model parameters using an evaluation function from the strategy.
initialize_parameters
(client_manager)Initialize global model parameters using given strategy.
- aggregate_evaluate(server_round: int, results: list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.EvaluateRes]], failures: list[Union[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.EvaluateRes], BaseException]]) tuple[Optional[float], dict[str, Union[bool, bytes, float, int, str]]] #
Aggregate evaluation losses using the given strategy.
- aggregate_fit(server_round: int, results: list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.FitRes]], failures: list[Union[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.FitRes], BaseException]]) tuple[Optional[flwr.common.typing.Parameters], dict[str, Union[bool, bytes, float, int, str]]] [소스]#
Aggregate training results as in DPFedAvgFixed and update clip norms.
- configure_evaluate(server_round: int, parameters: Parameters, client_manager: ClientManager) list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.EvaluateIns]] #
Configure the next round of evaluation using the specified strategy.
- 매개변수:
server_round (int) – The current round of federated learning.
parameters (Parameters) – 현재(전역) 모델 매개변수입니다.
client_manager (ClientManager) – The client manager which holds all currently connected clients.
- 반환:
evaluate_configuration – A list of tuples. Each tuple in the list identifies a ClientProxy and the EvaluateIns for this particular ClientProxy. If a particular ClientProxy is not included in this list, it means that this ClientProxy will not participate in the next round of federated evaluation.
- 반환 형식:
List[Tuple[ClientProxy, EvaluateIns]]
- configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.FitIns]] [소스]#
Configure the next round of training.
- evaluate(server_round: int, parameters: Parameters) tuple[float, dict[str, Union[bool, bytes, float, int, str]]] | None #
Evaluate model parameters using an evaluation function from the strategy.
- initialize_parameters(client_manager: ClientManager) Parameters | None #
Initialize global model parameters using given strategy.