# 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 XGBoost cyclic aggregation strategy."""
from logging import WARNING
from typing import Any, Dict, List, Optional, Tuple, Union, cast
from flwr.common import EvaluateIns, EvaluateRes, FitIns, FitRes, Parameters, Scalar
from flwr.common.logger import log
from flwr.server.client_manager import ClientManager
from flwr.server.client_proxy import ClientProxy
from .fedavg import FedAvg
[文档]class FedXgbCyclic(FedAvg):
"""Configurable FedXgbCyclic strategy implementation."""
# pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long
def __init__(
self,
**kwargs: Any,
):
self.global_model: Optional[bytes] = None
super().__init__(**kwargs)
def __repr__(self) -> str:
"""Compute a string representation of the strategy."""
rep = f"FedXgbCyclic(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 bagging."""
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, {}
# Fetch the client model from last round as global model
for _, fit_res in results:
update = fit_res.parameters.tensors
for bst in update:
self.global_model = bst
return (
Parameters(tensor_type="", tensors=[cast(bytes, self.global_model)]),
{},
)
[文档] def aggregate_evaluate(
self,
server_round: int,
results: List[Tuple[ClientProxy, EvaluateRes]],
failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
) -> Tuple[Optional[float], Dict[str, Scalar]]:
"""Aggregate evaluation metrics using average."""
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, {}
# Aggregate custom metrics if aggregation fn was provided
metrics_aggregated = {}
if self.evaluate_metrics_aggregation_fn:
eval_metrics = [(res.num_examples, res.metrics) for _, res in results]
metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics)
elif server_round == 1: # Only log this warning once
log(WARNING, "No evaluate_metrics_aggregation_fn provided")
return 0, metrics_aggregated