Code source de flwr.server.strategy.fedyogi

# 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
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# ==============================================================================
"""Adaptive Federated Optimization using Yogi (FedYogi) [Reddi et al., 2020] strategy.

Paper: arxiv.org/abs/2003.00295
"""


from typing import Callable, Dict, List, Optional, Tuple, Union

import numpy as np

from flwr.common import (
    FitRes,
    MetricsAggregationFn,
    NDArrays,
    Parameters,
    Scalar,
    ndarrays_to_parameters,
    parameters_to_ndarrays,
)
from flwr.server.client_proxy import ClientProxy

from .fedopt import FedOpt


# pylint: disable=line-too-long
[docs]class FedYogi(FedOpt): """FedYogi [Reddi et al., 2020] 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-2. eta_l : float, optional Client-side learning rate. Defaults to 0.0316. beta_1 : float, optional Momentum parameter. Defaults to 0.9. beta_2 : float, optional Second moment parameter. Defaults to 0.99. tau : float, optional Controls the algorithm's degree of adaptability. Defaults to 1e-3. """ # 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-2, eta_l: float = 0.0316, beta_1: float = 0.9, beta_2: float = 0.99, tau: float = 1e-3, ) -> 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, eta=eta, eta_l=eta_l, beta_1=beta_1, beta_2=beta_2, tau=tau, ) def __repr__(self) -> str: """Compute a string representation of the strategy.""" rep = f"FedYogi(accept_failures={self.accept_failures})" return rep
[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.""" fedavg_parameters_aggregated, metrics_aggregated = super().aggregate_fit( server_round=server_round, results=results, failures=failures ) if fedavg_parameters_aggregated is None: return None, {} fedavg_weights_aggregate = parameters_to_ndarrays(fedavg_parameters_aggregated) # Yogi delta_t: NDArrays = [ x - y for x, y in zip(fedavg_weights_aggregate, self.current_weights) ] # m_t if not self.m_t: self.m_t = [np.zeros_like(x) for x in delta_t] self.m_t = [ np.multiply(self.beta_1, x) + (1 - self.beta_1) * y for x, y in zip(self.m_t, delta_t) ] # v_t if not self.v_t: self.v_t = [np.zeros_like(x) for x in delta_t] self.v_t = [ x - (1.0 - self.beta_2) * np.multiply(y, y) * np.sign(x - np.multiply(y, y)) for x, y in zip(self.v_t, delta_t) ] new_weights = [ x + self.eta * y / (np.sqrt(z) + self.tau) for x, y, z in zip(self.current_weights, self.m_t, self.v_t) ] self.current_weights = new_weights return ndarrays_to_parameters(self.current_weights), metrics_aggregated