Code source de flwr.server.strategy.fedopt

# Copyright 2021 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""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


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