# Copyright 2022 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 Averaging with Momentum (FedAvgM) [Hsu et al., 2019] strategy.
Paper: arxiv.org/pdf/1909.06335.pdf
"""
from logging import WARNING
from typing import Callable, Optional, Union
from flwr.common import (
FitRes,
MetricsAggregationFn,
NDArrays,
Parameters,
Scalar,
ndarrays_to_parameters,
parameters_to_ndarrays,
)
from flwr.common.logger import log
from flwr.server.client_manager import ClientManager
from flwr.server.client_proxy import ClientProxy
from .aggregate import aggregate
from .fedavg import FedAvg
# pylint: disable=line-too-long
[docs]
class FedAvgM(FedAvg):
"""Federated Averaging with Momentum strategy.
Implementation based on https://arxiv.org/abs/1909.06335
Parameters
----------
fraction_fit : float, optional
Fraction of clients used during training. Defaults to 1.0.
fraction_evaluate : float, optional
Fraction of clients used during validation. Defaults to 1.0.
min_fit_clients : int, optional
Minimum number of clients used during training. Defaults to 2.
min_evaluate_clients : int, optional
Minimum number of clients used during validation. Defaults to 2.
min_available_clients : int, optional
Minimum number of total clients in the system. Defaults to 2.
evaluate_fn : Optional[Callable[[int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]]]
Optional function used for validation. Defaults to None.
on_fit_config_fn : Callable[[int], Dict[str, Scalar]], optional
Function used to configure training. Defaults to None.
on_evaluate_config_fn : Callable[[int], Dict[str, Scalar]], optional
Function used to configure validation. Defaults to None.
accept_failures : bool, optional
Whether or not accept rounds containing failures. Defaults to True.
initial_parameters : Parameters, optional
Initial global model parameters.
server_learning_rate: float
Server-side learning rate used in server-side optimization.
Defaults to 1.0.
server_momentum: float
Server-side momentum factor used for FedAvgM. Defaults to 0.0.
"""
# pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long
def __init__(
self,
*,
fraction_fit: float = 1.0,
fraction_evaluate: float = 1.0,
min_fit_clients: int = 2,
min_evaluate_clients: int = 2,
min_available_clients: int = 2,
evaluate_fn: Optional[
Callable[
[int, NDArrays, dict[str, Scalar]],
Optional[tuple[float, dict[str, Scalar]]],
]
] = None,
on_fit_config_fn: Optional[Callable[[int], dict[str, Scalar]]] = None,
on_evaluate_config_fn: Optional[Callable[[int], dict[str, Scalar]]] = None,
accept_failures: bool = True,
initial_parameters: Optional[Parameters] = None,
fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
server_learning_rate: float = 1.0,
server_momentum: float = 0.0,
) -> None:
super().__init__(
fraction_fit=fraction_fit,
fraction_evaluate=fraction_evaluate,
min_fit_clients=min_fit_clients,
min_evaluate_clients=min_evaluate_clients,
min_available_clients=min_available_clients,
evaluate_fn=evaluate_fn,
on_fit_config_fn=on_fit_config_fn,
on_evaluate_config_fn=on_evaluate_config_fn,
accept_failures=accept_failures,
initial_parameters=initial_parameters,
fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,
evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn,
)
self.server_learning_rate = server_learning_rate
self.server_momentum = server_momentum
self.server_opt: bool = (self.server_momentum != 0.0) or (
self.server_learning_rate != 1.0
)
self.momentum_vector: Optional[NDArrays] = None
def __repr__(self) -> str:
"""Compute a string representation of the strategy."""
rep = f"FedAvgM(accept_failures={self.accept_failures})"
return rep
[docs]
def initialize_parameters(
self, client_manager: ClientManager
) -> Optional[Parameters]:
"""Initialize global model parameters."""
return self.initial_parameters
[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 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), fit_res.num_examples)
for _, fit_res in results
]
fedavg_result = aggregate(weights_results)
# following convention described in
# https://pytorch.org/docs/stable/generated/torch.optim.SGD.html
if self.server_opt:
# You need to initialize the model
assert (
self.initial_parameters is not None
), "When using server-side optimization, model needs to be initialized."
initial_weights = parameters_to_ndarrays(self.initial_parameters)
# remember that updates are the opposite of gradients
pseudo_gradient: NDArrays = [
x - y
for x, y in zip(
parameters_to_ndarrays(self.initial_parameters), fedavg_result
)
]
if self.server_momentum > 0.0:
if server_round > 1:
assert (
self.momentum_vector
), "Momentum should have been created on round 1."
self.momentum_vector = [
self.server_momentum * x + y
for x, y in zip(self.momentum_vector, pseudo_gradient)
]
else:
self.momentum_vector = pseudo_gradient
# No nesterov for now
pseudo_gradient = self.momentum_vector
# SGD
fedavg_result = [
x - self.server_learning_rate * y
for x, y in zip(initial_weights, pseudo_gradient)
]
# Update current weights
self.initial_parameters = ndarrays_to_parameters(fedavg_result)
parameters_aggregated = ndarrays_to_parameters(fedavg_result)
# 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