# Copyright 2020 Adap 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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# ==============================================================================
"""Flower client (abstract base class)."""
from abc import ABC, abstractmethod
from flwr.common import EvaluateIns, EvaluateRes, FitIns, FitRes, ParametersRes
[docs]class Client(ABC):
"""Abstract base class for Flower clients."""
[docs] @abstractmethod
def get_parameters(self) -> ParametersRes:
"""Return the current local model parameters.
Returns:
ParametersRes: The current local model parameters.
"""
[docs] @abstractmethod
def fit(self, ins: FitIns) -> FitRes:
"""Refine the provided weights using the locally held dataset.
Args:
ins (FitIns): The training instructions containing (global) model
parameters received from the server and a dictionary of
configuration values used to customize the local training
process.
Returns:
FitRes: The training result containing updated parameters and other
details such as the number of local training examples used for
training.
"""
[docs] @abstractmethod
def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
"""Evaluate the provided weights using the locally held dataset.
Args:
ins (EvaluateIns): The evaluation instructions containing (global)
model parameters received from the server and a dictionary of
configuration values used to customize the local evaluation
process.
Returns:
EvaluateRes: The evaluation result containing the loss on the local
dataset and other details such as the number of local data
examples used for evaluation.
"""
