flwr.server.strategy.fedmedian 源代码

# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Federated Median (FedMedian) [Yin et al., 2018] strategy.

Paper: arxiv.org/pdf/1803.01498v1.pdf
"""


from logging import WARNING
from typing import Dict, List, Optional, Tuple, Union

from flwr.common import (
    FitRes,
    Parameters,
    Scalar,
    ndarrays_to_parameters,
    parameters_to_ndarrays,
)
from flwr.common.logger import log
from flwr.server.client_proxy import ClientProxy

from .aggregate import aggregate_median
from .fedavg import FedAvg


[文档]class FedMedian(FedAvg): """Configurable FedMedian strategy implementation.""" def __repr__(self) -> str: """Compute a string representation of the strategy.""" rep = f"FedMedian(accept_failures={self.accept_failures})" return rep
[文档] def aggregate_fit( self, server_round: int, results: List[Tuple[ClientProxy, FitRes]], failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]], ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]: """Aggregate fit results using median.""" if not results: return None, {} # Do not aggregate if there are failures and failures are not accepted if not self.accept_failures and failures: return None, {} # Convert results weights_results = [ (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples) for _, fit_res in results ] parameters_aggregated = ndarrays_to_parameters( aggregate_median(weights_results) ) # Aggregate custom metrics if aggregation fn was provided metrics_aggregated = {} if self.fit_metrics_aggregation_fn: fit_metrics = [(res.num_examples, res.metrics) for _, res in results] metrics_aggregated = self.fit_metrics_aggregation_fn(fit_metrics) elif server_round == 1: # Only log this warning once log(WARNING, "No fit_metrics_aggregation_fn provided") return parameters_aggregated, metrics_aggregated