FedProx

class FedProx(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, proximal_mu: float = 0.0)

Bases: FedAvg

联邦优化策略。

实施基于 https://arxiv.org/abs/1812.06127

FedProx extends FedAvg by introducing a proximal term into the client-side optimization objective. The strategy itself behaves identically to FedAvg on the server side, but each client MUST add a proximal regularization term to its local loss function during training:

\[\frac{\mu}{2} || w - w^t ||^2\]

Where $w^t$ denotes the global parameters and $w$ denotes the local weights being optimized.

This strategy sends the proximal term inside the ConfigRecord as part of the configure_train method under key "proximal-mu". The client can then use this value to add the proximal term to the loss function.

例如，在 PyTorch 中，损失将从：

致：

With global_params being a copy of the model parameters, created after applying the received global weights but before local training begins.

参数:

• 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.

• proximal_mu (float (default: 0.0)) -- 优化中使用的近端项权重。0.0 使该策略等同于 FedAvg，系数越大，使用的正则化就越多（也就是说，在训练过程中，客户端参数需要更接近服务器参数）。

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]

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) → 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.

参数:

• 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.

返回:

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

返回类型:

Results

summary() → None

Log summary configuration of the strategy.