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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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# ==============================================================================
"""Federated Averaging with Momentum (FedAvgM) [Hsu et al., 2019] strategy.
Paper: arxiv.org/pdf/1909.06335.pdf
"""
from collections import OrderedDict
from collections.abc import Iterable
from logging import INFO
from typing import Callable, Optional
from flwr.common import (
Array,
ArrayRecord,
ConfigRecord,
Message,
MetricRecord,
NDArrays,
RecordDict,
log,
)
from flwr.server import Grid
from ..exception import AggregationError
from .fedavg import FedAvg
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class FedAvgM(FedAvg):
"""Federated Averaging with Momentum strategy.
Implementation based on https://arxiv.org/abs/1909.06335
Parameters
----------
fraction_train : float (default: 1.0)
Fraction of nodes used during training. In case `min_train_nodes`
is larger than `fraction_train * total_connected_nodes`, `min_train_nodes`
will still be sampled.
fraction_evaluate : float (default: 1.0)
Fraction of nodes used during validation. In case `min_evaluate_nodes`
is larger than `fraction_evaluate * total_connected_nodes`,
`min_evaluate_nodes` will still be sampled.
min_train_nodes : int (default: 2)
Minimum number of nodes used during training.
min_evaluate_nodes : int (default: 2)
Minimum number of nodes used during validation.
min_available_nodes : int (default: 2)
Minimum number of total nodes in the system.
weighted_by_key : str (default: "num-examples")
The key within each MetricRecord whose value is used as the weight when
computing weighted averages for both ArrayRecords and MetricRecords.
arrayrecord_key : str (default: "arrays")
Key used to store the ArrayRecord when constructing Messages.
configrecord_key : str (default: "config")
Key used to store the ConfigRecord when constructing Messages.
train_metrics_aggr_fn : Optional[callable] (default: None)
Function with signature (list[RecordDict], str) -> MetricRecord,
used to aggregate MetricRecords from training round replies.
If `None`, defaults to `aggregate_metricrecords`, which performs a weighted
average using the provided weight factor key.
evaluate_metrics_aggr_fn : Optional[callable] (default: None)
Function with signature (list[RecordDict], str) -> MetricRecord,
used to aggregate MetricRecords from training round replies.
If `None`, defaults to `aggregate_metricrecords`, which performs a weighted
average using the provided weight factor key.
server_learning_rate: float (default: 1.0)
Server-side learning rate used in server-side optimization.
server_momentum: float (default: 0.0)
Server-side momentum factor used for FedAvgM.
"""
def __init__( # pylint: disable=R0913, R0917
self,
fraction_train: float = 1.0,
fraction_evaluate: float = 1.0,
min_train_nodes: int = 2,
min_evaluate_nodes: int = 2,
min_available_nodes: int = 2,
weighted_by_key: str = "num-examples",
arrayrecord_key: str = "arrays",
configrecord_key: str = "config",
train_metrics_aggr_fn: Optional[
Callable[[list[RecordDict], str], MetricRecord]
] = None,
evaluate_metrics_aggr_fn: Optional[
Callable[[list[RecordDict], str], MetricRecord]
] = None,
server_learning_rate: float = 1.0,
server_momentum: float = 0.0,
) -> None:
super().__init__(
fraction_train=fraction_train,
fraction_evaluate=fraction_evaluate,
min_train_nodes=min_train_nodes,
min_evaluate_nodes=min_evaluate_nodes,
min_available_nodes=min_available_nodes,
weighted_by_key=weighted_by_key,
arrayrecord_key=arrayrecord_key,
configrecord_key=configrecord_key,
train_metrics_aggr_fn=train_metrics_aggr_fn,
evaluate_metrics_aggr_fn=evaluate_metrics_aggr_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.current_arrays: Optional[ArrayRecord] = None
self.momentum_vector: Optional[NDArrays] = None
[๋ฌธ์]
def summary(self) -> None:
"""Log summary configuration of the strategy."""
opt_status = "ON" if self.server_opt else "OFF"
log(INFO, "\tโโโ> FedAvgM settings:")
log(INFO, "\t|\tโโโ Server optimization: %s", opt_status)
log(INFO, "\t|\tโโโ Server learning rate: %s", self.server_learning_rate)
log(INFO, "\t|\tโโโ Server Momentum: %s", self.server_momentum)
super().summary()
[๋ฌธ์]
def aggregate_train(
self,
server_round: int,
replies: Iterable[Message],
) -> tuple[Optional[ArrayRecord], Optional[MetricRecord]]:
"""Aggregate ArrayRecords and MetricRecords in the received Messages."""
# Call FedAvg aggregate_train to perform validation and aggregation
aggregated_arrays, aggregated_metrics = super().aggregate_train(
server_round, replies
)
# following convention described in
# https://pytorch.org/docs/stable/generated/torch.optim.SGD.html
if self.server_opt and aggregated_arrays is not None:
# The initial parameters should be set in `start()` method already
if self.current_arrays is None:
raise AggregationError(
"No initial parameters set for FedAvgM. "
"Ensure that `configure_train` has been called before aggregation."
)
ndarrays = self.current_arrays.to_numpy_ndarrays()
aggregated_ndarrays = aggregated_arrays.to_numpy_ndarrays()
# Preserve keys for arrays in ArrayRecord
array_keys = list(aggregated_arrays.keys())
aggregated_arrays.clear()
# Remember that updates are the opposite of gradients
pseudo_gradient = [
old - new for new, old in zip(aggregated_ndarrays, ndarrays)
]
if self.server_momentum > 0.0:
if self.momentum_vector is None:
# Initialize momentum vector in the first round
self.momentum_vector = pseudo_gradient
else:
self.momentum_vector = [
self.server_momentum * mv + pg
for mv, pg in zip(self.momentum_vector, pseudo_gradient)
]
# No nesterov for now
pseudo_gradient = self.momentum_vector
# SGD and convert back to ArrayRecord
updated_array_list = [
Array(old - self.server_learning_rate * pg)
for old, pg in zip(ndarrays, pseudo_gradient)
]
aggregated_arrays = ArrayRecord(
OrderedDict(zip(array_keys, updated_array_list))
)
# Update current weights
self.current_arrays = aggregated_arrays
return aggregated_arrays, aggregated_metrics