DifferentialPrivacyClientSideFixedClipping#

class DifferentialPrivacyClientSideFixedClipping(strategy: Strategy, noise_multiplier: float, clipping_norm: float, num_sampled_clients: int)[源代码]#

基类：Strategy

Strategy wrapper for central DP with client-side fixed clipping.

Use fixedclipping_mod modifier at the client side.

In comparison to DifferentialPrivacyServerSideFixedClipping, which performs clipping on the server-side, DifferentialPrivacyClientSideFixedClipping expects clipping to happen on the client-side, usually by using the built-in fixedclipping_mod.

参数:

  • strategy (Strategy) -- The strategy to which DP functionalities will be added by this wrapper.

  • noise_multiplier (float) -- The noise multiplier for the Gaussian mechanism for model updates. A value of 1.0 or higher is recommended for strong privacy.

  • clipping_norm (float) -- The value of the clipping norm.

  • num_sampled_clients (int) -- The number of clients that are sampled on each round.

示例

Create a strategy:

>>> strategy = fl.server.strategy.FedAvg(...)

Wrap the strategy with the DifferentialPrivacyClientSideFixedClipping wrapper:

>>> dp_strategy = DifferentialPrivacyClientSideFixedClipping(
>>>     strategy, cfg.noise_multiplier, cfg.clipping_norm, cfg.num_sampled_clients
>>> )

On the client, add the fixedclipping_mod to the client-side mods:

>>> app = fl.client.ClientApp(
>>>     client_fn=client_fn, mods=[fixedclipping_mod]
>>> )

Methods

aggregate_evaluate(server_round, results, ...)

使用给定的策略汇总评估损失。

aggregate_fit(server_round, results, failures)

Add noise to the aggregated parameters.

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)

使用策略中的评估函数评估模型参数。

initialize_parameters(client_manager)

使用给定的策略初始化全局模型参数。
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]][源代码]#

使用给定的策略汇总评估损失。

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]][源代码]#

Add noise to the aggregated parameters.

configure_evaluate(server_round: int, parameters: Parameters, client_manager: ClientManager) → List[Tuple[ClientProxy, EvaluateIns]][源代码]#

配置下一轮评估。

configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) → List[Tuple[ClientProxy, FitIns]][源代码]#

配置下一轮训练。

evaluate(server_round: int, parameters: Parameters) → Tuple[float, Dict[str, bool | bytes | float | int | str]] | None[源代码]#

使用策略中的评估函数评估模型参数。

initialize_parameters(client_manager: ClientManager) → Parameters | None[源代码]#

使用给定的策略初始化全局模型参数。