flwr.server.strategy.fedavg의 소스 코드

# Copyright 2025 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,
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# ==============================================================================
"""Federated Averaging (FedAvg) [McMahan et al., 2016] strategy.

Paper: arxiv.org/abs/1602.05629
"""


from logging import WARNING
from typing import Callable, Optional, Union

from flwr.common import (
    EvaluateIns,
    EvaluateRes,
    FitIns,
    FitRes,
    MetricsAggregationFn,
    NDArrays,
    Parameters,
    Scalar,
    ndarrays_to_parameters,
    parameters_to_ndarrays,
)
from flwr.common.logger import log
from flwr.server.client_manager import ClientManager
from flwr.server.client_proxy import ClientProxy

from .aggregate import aggregate, aggregate_inplace, weighted_loss_avg
from .strategy import Strategy

WARNING_MIN_AVAILABLE_CLIENTS_TOO_LOW = """
Setting `min_available_clients` lower than `min_fit_clients` or
`min_evaluate_clients` can cause the server to fail when there are too few clients
connected to the server. `min_available_clients` must be set to a value larger
than or equal to the values of `min_fit_clients` and `min_evaluate_clients`.
"""


# pylint: disable=line-too-long


class FedAvg(Strategy):
    """Federated Averaging strategy.

    Implementation based on https://arxiv.org/abs/1602.05629

    Parameters
    ----------
    fraction_fit : float, optional
        Fraction of clients used during training. In case `min_fit_clients`
        is larger than `fraction_fit * available_clients`, `min_fit_clients`
        will still be sampled. Defaults to 1.0.
    fraction_evaluate : float, optional
        Fraction of clients used during validation. In case `min_evaluate_clients`
        is larger than `fraction_evaluate * available_clients`,
        `min_evaluate_clients` will still be sampled. 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, optional
        Initial global model parameters.
    fit_metrics_aggregation_fn : Optional[MetricsAggregationFn]
        Metrics aggregation function, optional.
    evaluate_metrics_aggregation_fn : Optional[MetricsAggregationFn]
        Metrics aggregation function, optional.
    inplace : bool (default: True)
        Enable (True) or disable (False) in-place aggregation of model updates.
    """

    # pylint: disable=too-many-arguments,too-many-instance-attributes, 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: Optional[Parameters] = None,
        fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
        evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
        inplace: bool = True,
    ) -> None:
        super().__init__()

        if (
            min_fit_clients > min_available_clients
            or min_evaluate_clients > min_available_clients
        ):
            log(WARNING, WARNING_MIN_AVAILABLE_CLIENTS_TOO_LOW)

        self.fraction_fit = fraction_fit
        self.fraction_evaluate = fraction_evaluate
        self.min_fit_clients = min_fit_clients
        self.min_evaluate_clients = min_evaluate_clients
        self.min_available_clients = min_available_clients
        self.evaluate_fn = evaluate_fn
        self.on_fit_config_fn = on_fit_config_fn
        self.on_evaluate_config_fn = on_evaluate_config_fn
        self.accept_failures = accept_failures
        self.initial_parameters = initial_parameters
        self.fit_metrics_aggregation_fn = fit_metrics_aggregation_fn
        self.evaluate_metrics_aggregation_fn = evaluate_metrics_aggregation_fn
        self.inplace = inplace

    def __repr__(self) -> str:
        """Compute a string representation of the strategy."""
        rep = f"FedAvg(accept_failures={self.accept_failures})"
        return rep



    def num_fit_clients(self, num_available_clients: int) -> tuple[int, int]:
        """Return the sample size and the required number of available clients."""
        num_clients = int(num_available_clients * self.fraction_fit)
        return max(num_clients, self.min_fit_clients), self.min_available_clients




    def num_evaluation_clients(self, num_available_clients: int) -> tuple[int, int]:
        """Use a fraction of available clients for evaluation."""
        num_clients = int(num_available_clients * self.fraction_evaluate)
        return max(num_clients, self.min_evaluate_clients), self.min_available_clients




    def initialize_parameters(
        self, client_manager: ClientManager
    ) -> Optional[Parameters]:
        """Initialize global model parameters."""
        initial_parameters = self.initial_parameters
        self.initial_parameters = None  # Don't keep initial parameters in memory
        return initial_parameters




    def evaluate(
        self, server_round: int, parameters: Parameters
    ) -> Optional[tuple[float, dict[str, Scalar]]]:
        """Evaluate model parameters using an evaluation function."""
        if self.evaluate_fn is None:
            # No evaluation function provided
            return None
        parameters_ndarrays = parameters_to_ndarrays(parameters)
        eval_res = self.evaluate_fn(server_round, parameters_ndarrays, {})
        if eval_res is None:
            return None
        loss, metrics = eval_res
        return loss, metrics




    def configure_fit(
        self, server_round: int, parameters: Parameters, client_manager: ClientManager
    ) -> list[tuple[ClientProxy, FitIns]]:
        """Configure the next round of training."""
        config = {}
        if self.on_fit_config_fn is not None:
            # Custom fit config function provided
            config = self.on_fit_config_fn(server_round)
        fit_ins = FitIns(parameters, config)

        # Sample clients
        sample_size, min_num_clients = self.num_fit_clients(
            client_manager.num_available()
        )
        clients = client_manager.sample(
            num_clients=sample_size, min_num_clients=min_num_clients
        )

        # Return client/config pairs
        return [(client, fit_ins) for client in clients]




    def configure_evaluate(
        self, server_round: int, parameters: Parameters, client_manager: ClientManager
    ) -> list[tuple[ClientProxy, EvaluateIns]]:
        """Configure the next round of evaluation."""
        # Do not configure federated evaluation if fraction eval is 0.
        if self.fraction_evaluate == 0.0:
            return []

        # Parameters and config
        config = {}
        if self.on_evaluate_config_fn is not None:
            # Custom evaluation config function provided
            config = self.on_evaluate_config_fn(server_round)
        evaluate_ins = EvaluateIns(parameters, config)

        # Sample clients
        sample_size, min_num_clients = self.num_evaluation_clients(
            client_manager.num_available()
        )
        clients = client_manager.sample(
            num_clients=sample_size, min_num_clients=min_num_clients
        )

        # Return client/config pairs
        return [(client, evaluate_ins) for client in clients]




    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."""
        if not results:
            return None, {}
        # Do not aggregate if there are failures and failures are not accepted
        if not self.accept_failures and failures:
            return None, {}

        if self.inplace:
            # Does in-place weighted average of results
            aggregated_ndarrays = aggregate_inplace(results)
        else:
            # Convert results
            weights_results = [
                (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
                for _, fit_res in results
            ]
            aggregated_ndarrays = aggregate(weights_results)

        parameters_aggregated = ndarrays_to_parameters(aggregated_ndarrays)

        # Aggregate custom metrics if aggregation fn was provided
        metrics_aggregated = {}
        if self.fit_metrics_aggregation_fn:
            fit_metrics = [(res.num_examples, res.metrics) for _, res in results]
            metrics_aggregated = self.fit_metrics_aggregation_fn(fit_metrics)
        elif server_round == 1:  # Only log this warning once
            log(WARNING, "No fit_metrics_aggregation_fn provided")

        return parameters_aggregated, metrics_aggregated




    def aggregate_evaluate(
        self,
        server_round: int,
        results: list[tuple[ClientProxy, EvaluateRes]],
        failures: list[Union[tuple[ClientProxy, EvaluateRes], BaseException]],
    ) -> tuple[Optional[float], dict[str, Scalar]]:
        """Aggregate evaluation losses using weighted average."""
        if not results:
            return None, {}
        # Do not aggregate if there are failures and failures are not accepted
        if not self.accept_failures and failures:
            return None, {}

        # Aggregate loss
        loss_aggregated = weighted_loss_avg(
            [
                (evaluate_res.num_examples, evaluate_res.loss)
                for _, evaluate_res in results
            ]
        )

        # Aggregate custom metrics if aggregation fn was provided
        metrics_aggregated = {}
        if self.evaluate_metrics_aggregation_fn:
            eval_metrics = [(res.num_examples, res.metrics) for _, res in results]
            metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics)
        elif server_round == 1:  # Only log this warning once
            log(WARNING, "No evaluate_metrics_aggregation_fn provided")

        return loss_aggregated, metrics_aggregated