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: 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][source]¶

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][source]¶

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.

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)) – 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.

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[source]¶

Log summary configuration of the strategy.