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)

Bases: DifferentialPrivacyAdaptiveBase

Message-based central DP with server-side adaptive clipping.

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, should_stop: Callable[[int, Result], bool] | 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.

Parameters:

• 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)) – Maximum number of federated learning rounds to execute.

• timeout (float (default: 3600)) – Timeout in seconds for waiting for node responses. Outstanding responses from nodes that become unavailable are replaced with error replies.

• 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.

• should_stop (Callable[[int, Result], bool], optional) – Optional predicate to determine whether to stop training early. Called at the end of each round with the round number and accumulated results. Return True to stop and return the model and metrics collected so far. Not called for round zero. Results must not be modified. Defaults to None (no early stopping).

Returns:

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

Return type:

Results

summary() → None

Log summary configuration of the strategy.