DifferentialPrivacyServerSideAdaptiveClipping#

class DifferentialPrivacyServerSideAdaptiveClipping(strategy: Strategy, noise_multiplier: float, num_sampled_clients: int, initial_clipping_norm: float = 0.1, target_clipped_quantile: float = 0.5, clip_norm_lr: float = 0.2, clipped_count_stddev: float | None = None)[source]#

Bases : Strategy

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

Paramètres:

  • 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.

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

  • initial_clipping_norm (float) – The initial value of clipping norm. Defaults to 0.1. Andrew et al. recommends to set to 0.1.

  • target_clipped_quantile (float) – The desired quantile of updates which should be clipped. Defaults to 0.5.

  • clip_norm_lr (float) – The learning rate for the clipping norm adaptation. Defaults to 0.2. Andrew et al. recommends to set to 0.2.

  • clipped_count_stddev (float) – The standard deviation of the noise added to the count of updates below the estimate. Andrew et al. recommends to set to expected_num_records/20

Exemples

Create a strategy:

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

Wrap the strategy with the DifferentialPrivacyServerSideAdaptiveClipping wrapper

>>> dp_strategy = DifferentialPrivacyServerSideAdaptiveClipping(
>>>     strategy, cfg.noise_multiplier, cfg.num_sampled_clients, ...
>>> )

Methods

aggregate_evaluate(server_round, results, ...)

Aggregate evaluation losses using the given strategy.

aggregate_fit(server_round, results, failures)

Aggregate training results and update clip norms.

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]][source]#

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]][source]#

Aggregate training results and update clip norms.

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

Configure the next round of evaluation.

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

Configure the next round of training.

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

Evaluate model parameters using an evaluation function from the strategy.

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

Initialize global model parameters using given strategy.