flwr.server.strategy.krum 源代码

# 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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"""Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent.

[Blanchard et al., 2017].

Paper: proceedings.neurips.cc/paper/2017/file/f4b9ec30ad9f68f89b29639786cb62ef-Paper.pdf
"""


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

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

from .aggregate import aggregate_krum
from .fedavg import FedAvg


# pylint: disable=line-too-long
[文档]class Krum(FedAvg): """Krum [Blanchard et al., 2017] strategy. Implementation based on https://arxiv.org/abs/1703.02757 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. num_malicious_clients : int, optional Number of malicious clients in the system. Defaults to 0. num_clients_to_keep : int, optional Number of clients to keep before averaging (MultiKrum). Defaults to 0, in that case classical Krum is applied. 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. """ # pylint: disable=too-many-arguments,too-many-instance-attributes 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, num_malicious_clients: int = 0, num_clients_to_keep: int = 0, 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, ) -> 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.num_malicious_clients = num_malicious_clients self.num_clients_to_keep = num_clients_to_keep def __repr__(self) -> str: """Compute a string representation of the strategy.""" rep = f"Krum(accept_failures={self.accept_failures})" return rep
[文档] 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 Krum.""" 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, {} # Convert results weights_results = [ (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples) for _, fit_res in results ] parameters_aggregated = ndarrays_to_parameters( aggregate_krum( weights_results, self.num_malicious_clients, self.num_clients_to_keep ) ) # 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