DifferentialPrivacyClientSideAdaptiveClipping

class DifferentialPrivacyClientSideAdaptiveClipping(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)

Bases : Strategy

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

Use adaptiveclipping_mod modifier at the client side.

In comparison to DifferentialPrivacyServerSideAdaptiveClipping, which performs clipping on the server-side, DifferentialPrivacyClientSideAdaptiveClipping expects clipping to happen on the client-side, usually by using the built-in adaptiveclipping_mod.

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 stddev of the noise added to the count of updates currently 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 DifferentialPrivacyClientSideAdaptiveClipping wrapper:

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

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

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

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]]

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]]

Aggregate training results and update clip norms.

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.