DifferentialPrivacyServerSideFixedClipping

class DifferentialPrivacyServerSideFixedClipping(strategy: Strategy, noise_multiplier: float, clipping_norm: float, num_sampled_clients: int)

기반 클래스: Strategy

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

매개변수:

• 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) – 클리핑 기준값입니다.

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

예제

Create a strategy:

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

Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper

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

메소드

aggregate_evaluate(server_round, results, ...)	Aggregate evaluation losses using the given strategy.
aggregate_fit(server_round, results, failures)	Compute the updates, clip, and pass them for aggregation.
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 from the strategy.
initialize_parameters(client_manager)	Initialize global model parameters using given strategy.

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 the given strategy.

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]]

Compute the updates, clip, and pass them for aggregation.

Afterward, add noise to the aggregated parameters.

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: Parameters) → tuple[float, dict[str, 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.