Result¶

class Result(arrays: ~flwr.common.record.arrayrecord.ArrayRecord = <factory>, train_metrics_clientapp: dict[int, ~flwr.common.record.metricrecord.MetricRecord] = <factory>, evaluate_metrics_clientapp: dict[int, ~flwr.common.record.metricrecord.MetricRecord] = <factory>, evaluate_metrics_serverapp: dict[int, ~flwr.common.record.metricrecord.MetricRecord] = <factory>)[source]¶

Bases : object

Data class carrying records generated during the execution of a strategy.

This class encapsulates the results of a federated learning strategy execution, including the final global model parameters and metrics collected throughout the federated training and evaluation (both federated and centralized) stages.

arrays¶

The final global model parameters. Contains the aggregated model weights/parameters that resulted from the federated learning process.

Type:

ArrayRecord

train_metrics_clientapp¶

Training metrics collected from ClientApps, indexed by round number. Contains aggregated metrics (e.g., loss, accuracy) from the training phase of each federated learning round.

Type:

dict[int, MetricRecord]

evaluate_metrics_clientapp¶

Evaluation metrics collected from ClientApps, indexed by round number. Contains aggregated metrics (e.g. validation loss) from the evaluation phase where ClientApps evaluate the global model on their local validation/test data.

Type:

dict[int, MetricRecord]

evaluate_metrics_serverapp¶

Evaluation metrics generated at the ServerApp, indexed by round number. Contains metrics from centralized evaluation performed by the ServerApp (e.g., when the server evaluates the global model on a held-out dataset).

Type:

dict[int, MetricRecord]

Methods

Attributes