FedAvg

class FedAvg(fraction_train: float = 1.0, fraction_evaluate: float = 1.0, min_train_nodes: int = 2, min_evaluate_nodes: int = 2, min_available_nodes: int = 2, weighted_by_key: str = 'num-examples', arrayrecord_key: str = 'arrays', configrecord_key: str = 'config', train_metrics_aggr_fn: Callable[[list[RecordDict], str], MetricRecord] | None = None, evaluate_metrics_aggr_fn: Callable[[list[RecordDict], str], MetricRecord] | None = None)[source]

Bases: Strategy

Federated Averaging strategy.

Implementation based on https://arxiv.org/abs/1602.05629

Parameters:

  • fraction_train (float (default: 1.0)) – Fraction of nodes used during training. In case min_train_nodes is larger than fraction_train * total_connected_nodes, min_train_nodes will still be sampled.

  • fraction_evaluate (float (default: 1.0)) – Fraction of nodes used during validation. In case min_evaluate_nodes is larger than fraction_evaluate * total_connected_nodes, min_evaluate_nodes will still be sampled.

  • min_train_nodes (int (default: 2)) – Minimum number of nodes used during training.

  • min_evaluate_nodes (int (default: 2)) – Minimum number of nodes used during validation.

  • min_available_nodes (int (default: 2)) – Minimum number of total nodes in the system.

  • weighted_by_key (str (default: "num-examples")) – The key within each MetricRecord whose value is used as the weight when computing weighted averages for both ArrayRecords and MetricRecords.

  • arrayrecord_key (str (default: "arrays")) – Key used to store the ArrayRecord when constructing Messages.

  • configrecord_key (str (default: "config")) – Key used to store the ConfigRecord when constructing Messages.

  • train_metrics_aggr_fn (Optional[callable] (default: None)) – Function with signature (list[RecordDict], str) -> MetricRecord, used to aggregate MetricRecords from training round replies. If None, defaults to aggregate_metricrecords, which performs a weighted average using the provided weight factor key.

  • evaluate_metrics_aggr_fn (Optional[callable] (default: None)) – Function with signature (list[RecordDict], str) -> MetricRecord, used to aggregate MetricRecords from training round replies. If None, defaults to aggregate_metricrecords, which performs a weighted average using the provided weight factor key.

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 federated 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[source]

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][source]

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 federated 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) → 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], MetricRecord], optional) – Optional function for centralized evaluation of the global model. Takes server round number and array record, returns a MetricRecord. 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.