# 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 [Ma et al., 2023] strategy.
Strategy in the horizontal setting based on building Neural Network and averaging on
prediction outcomes.
Paper: arxiv.org/abs/2304.07537
"""
from logging import WARNING
from typing import Any, Dict, List, Optional, Tuple, Union
from flwr.common import FitRes, Scalar, ndarrays_to_parameters, parameters_to_ndarrays
from flwr.common.logger import log, warn_deprecated_feature
from flwr.server.client_proxy import ClientProxy
from .aggregate import aggregate
from .fedavg import FedAvg
[docs]class FedXgbNnAvg(FedAvg):
"""Configurable FedXgbNnAvg strategy implementation.
Warning
-------
This strategy is deprecated, but a copy of it is available in Flower Baselines:
https://github.com/adap/flower/tree/main/baselines/hfedxgboost.
"""
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Federated XGBoost [Ma et al., 2023] strategy.
Implementation based on https://arxiv.org/abs/2304.07537.
"""
super().__init__(*args, **kwargs)
warn_deprecated_feature("`FedXgbNnAvg` strategy")
def __repr__(self) -> str:
"""Compute a string representation of the strategy."""
rep = f"FedXgbNnAvg(accept_failures={self.accept_failures})"
return rep
[docs] def evaluate(
self, server_round: int, parameters: Any
) -> Optional[Tuple[float, Dict[str, Scalar]]]:
"""Evaluate model parameters using an evaluation function."""
if self.evaluate_fn is None:
# No evaluation function provided
return None
eval_res = self.evaluate_fn(server_round, parameters, {})
if eval_res is None:
return None
loss, metrics = eval_res
return loss, metrics
[docs] def aggregate_fit(
self,
server_round: int,
results: List[Tuple[ClientProxy, FitRes]],
failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
) -> Tuple[Optional[Any], Dict[str, Scalar]]:
"""Aggregate fit results using weighted 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, {}
# Convert results
weights_results = [
(
parameters_to_ndarrays(fit_res.parameters[0].parameters), # type: ignore # noqa: E501 # pylint: disable=line-too-long
fit_res.num_examples,
)
for _, fit_res in results
]
parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results))
# Aggregate XGBoost trees from all clients
trees_aggregated = [fit_res.parameters[1] for _, fit_res in results] # type: ignore # noqa: E501 # pylint: disable=line-too-long
# 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, trees_aggregated], metrics_aggregated