FedMedian

class FedMedian(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: FedAvg

Federated Median (FedMedian) strategy.

Implementation based on https://arxiv.org/pdf/1803.01498v1

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 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

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]

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 = 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.

  • 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.