Code source de flwr.server.strategy.fedavg

# Copyright 2020 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
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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, Dict, List, Optional, Tuple, 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
[docs]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
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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]
[docs] 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]
[docs] 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
[docs] 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