Code source de flwr.server.strategy.fedmedian
# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""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
[docs]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
[docs] 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