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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.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# ==============================================================================
"""Adaptive Federated Optimization (FedOpt) [Reddi et al., 2020] abstract strategy.
Paper: arxiv.org/abs/2003.00295
"""
from typing import Callable, Optional
from flwr.common import (
MetricsAggregationFn,
NDArrays,
Parameters,
Scalar,
parameters_to_ndarrays,
)
from .fedavg import FedAvg
# pylint: disable=line-too-long
[docs]
class FedOpt(FedAvg):
"""Federated Optim strategy.
Implementation based on https://arxiv.org/abs/2003.00295v5
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
Initial global model parameters.
fit_metrics_aggregation_fn : Optional[MetricsAggregationFn]
Metrics aggregation function, optional.
evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn]
Metrics aggregation function, optional.
eta : float, optional
Server-side learning rate. Defaults to 1e-1.
eta_l : float, optional
Client-side learning rate. Defaults to 1e-1.
beta_1 : float, optional
Momentum parameter. Defaults to 0.0.
beta_2 : float, optional
Second moment parameter. Defaults to 0.0.
tau : float, optional
Controls the algorithm's degree of adaptability. Defaults to 1e-9.
"""
# pylint: disable=too-many-arguments,too-many-instance-attributes,too-many-locals, 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: Parameters,
fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
eta: float = 1e-1,
eta_l: float = 1e-1,
beta_1: float = 0.0,
beta_2: float = 0.0,
tau: float = 1e-9,
) -> 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.current_weights = parameters_to_ndarrays(initial_parameters)
self.eta = eta
self.eta_l = eta_l
self.tau = tau
self.beta_1 = beta_1
self.beta_2 = beta_2
self.m_t: Optional[NDArrays] = None
self.v_t: Optional[NDArrays] = None
def __repr__(self) -> str:
"""Compute a string representation of the strategy."""
rep = f"FedOpt(accept_failures={self.accept_failures})"
return rep