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 : DifferentialPrivacyAdaptiveBase

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.serverapp.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(mods=[adaptiveclipping_mod])

Methods

aggregate_evaluate(server_round, replies)	Aggregate MetricRecords in the received Messages.
aggregate_train(server_round, replies)	Aggregate ArrayRecords and MetricRecords in the received Messages.
configure_evaluate(server_round, arrays, ...)	Configure the next round of federated evaluation.
configure_train(server_round, arrays, ...)	Configure the next round of training.
start(grid, initial_arrays[, num_rounds, ...])	Execute the federated learning strategy.
summary()	Log summary configuration of the strategy.

aggregate_evaluate(server_round: int, replies: Iterable[Message]) → MetricRecord | None

Aggregate MetricRecords in the received Messages.

aggregate_train(server_round: int, replies: Iterable[Message]) → tuple[ArrayRecord | None, MetricRecord | None]

Aggregate ArrayRecords and MetricRecords in the received Messages.

configure_evaluate(server_round: int, arrays: ArrayRecord, config: ConfigRecord, grid: Grid) → Iterable[Message]

Configure the next round of federated evaluation.

configure_train(server_round: int, arrays: ArrayRecord, config: ConfigRecord, grid: Grid) → Iterable[Message]

Configure the next round of training.

start(grid: Grid, initial_arrays: ArrayRecord, num_rounds: int = 3, timeout: float = 3600, train_config: ConfigRecord | None = None, evaluate_config: ConfigRecord | None = None, evaluate_fn: Callable[[int, ArrayRecord], MetricRecord | None] | None = None) → Result

Execute the federated learning strategy.

Runs the complete federated learning workflow for the specified number of rounds, including training, evaluation, and optional centralized evaluation.

Paramètres:

• grid (Grid) – The Grid instance used to send/receive Messages from nodes executing a ClientApp.

• initial_arrays (ArrayRecord) – Initial model parameters (arrays) to be used for federated learning.

• num_rounds (int (default: 3)) – Number of federated learning rounds to execute.

• timeout (float (default: 3600)) – Timeout in seconds for waiting for node responses.

• train_config (ConfigRecord, optional) – Configuration to be sent to nodes during training rounds. If unset, an empty ConfigRecord will be used.

• evaluate_config (ConfigRecord, optional) – Configuration to be sent to nodes during evaluation rounds. If unset, an empty ConfigRecord will be used.

• evaluate_fn (Callable[[int, ArrayRecord], Optional[MetricRecord]], optional) – Optional function for centralized evaluation of the global model. Takes server round number and array record, returns a MetricRecord or None. If provided, will be called before the first round and after each round. Defaults to None.

Renvoie:

Results containing final model arrays and also training metrics, evaluation metrics and global evaluation metrics (if provided) from all rounds.

Type renvoyé:

Results

summary() → None

Log summary configuration of the strategy.