Source code for flwr.server.strategy.fedopt

# Copyright 2020 Adap GmbH. All Rights Reserved.
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"""Adaptive Federated Optimization (FedOpt) [Reddi et al., 2020] abstract
strategy.

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


from typing import Callable, Dict, Optional, Tuple

from flwr.common import (
    MetricsAggregationFn,
    Parameters,
    Scalar,
    Weights,
    parameters_to_weights,
)

from .fedavg import FedAvg


[docs]class FedOpt(FedAvg): """Configurable FedAdagrad strategy implementation.""" # pylint: disable=too-many-arguments,too-many-instance-attributes,too-many-locals
[docs] def __init__( self, *, fraction_fit: float = 0.1, fraction_eval: float = 0.1, min_fit_clients: int = 2, min_eval_clients: int = 2, min_available_clients: int = 2, eval_fn: Optional[ Callable[[Weights], 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: """Federated Optim strategy interface. Implementation based on https://arxiv.org/abs/2003.00295v5 Parameters ---------- fraction_fit (float, optional): Fraction of clients used during training. Defaults to 0.1. fraction_eval (float, optional): Fraction of clients used during validation. Defaults to 0.1. min_fit_clients (int, optional): Minimum number of clients used during training. Defaults to 2. min_eval_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. eval_fn (Callable[[Weights], Optional[Tuple[float, float]]], optional): Function used for validation. Defaults to None. on_fit_config_fn (Callable[[int], Dict[str, str]], optional): Function used to configure training. Defaults to None. on_evaluate_config_fn (Callable[[int], Dict[str, str]], 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 set of parameters from the server. 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. """ super().__init__( fraction_fit=fraction_fit, fraction_eval=fraction_eval, min_fit_clients=min_fit_clients, min_eval_clients=min_eval_clients, min_available_clients=min_available_clients, eval_fn=eval_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_weights(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[Weights] = None self.v_t: Optional[Weights] = None
def __repr__(self) -> str: rep = f"FedOpt(accept_failures={self.accept_failures})" return rep