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.