Source code for flwr.server.strategy.fedopt

# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (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, Dict, Optional, Tuple

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