Code source de flwr.client.client

# 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
# limitations under the License.
# ==============================================================================
"""Flower client (abstract base class)."""

# Needed to `Client` class can return a type of `Client` (not needed in py3.11+)
from __future__ import annotations

from abc import ABC

from flwr.common import (
    Code,
    Context,
    EvaluateIns,
    EvaluateRes,
    FitIns,
    FitRes,
    GetParametersIns,
    GetParametersRes,
    GetPropertiesIns,
    GetPropertiesRes,
    Parameters,
    Status,
)


[docs]class Client(ABC): """Abstract base class for Flower clients.""" context: Context
[docs] def get_properties(self, ins: GetPropertiesIns) -> GetPropertiesRes: """Return set of client's properties. Parameters ---------- ins : GetPropertiesIns The get properties instructions received from the server containing a dictionary of configuration values. Returns ------- GetPropertiesRes The current client properties. """ _ = (self, ins) return GetPropertiesRes( status=Status( code=Code.GET_PROPERTIES_NOT_IMPLEMENTED, message="Client does not implement `get_properties`", ), properties={}, )
[docs] def get_parameters(self, ins: GetParametersIns) -> GetParametersRes: """Return the current local model parameters. Parameters ---------- ins : GetParametersIns The get parameters instructions received from the server containing a dictionary of configuration values. Returns ------- GetParametersRes The current local model parameters. """ _ = (self, ins) return GetParametersRes( status=Status( code=Code.GET_PARAMETERS_NOT_IMPLEMENTED, message="Client does not implement `get_parameters`", ), parameters=Parameters(tensor_type="", tensors=[]), )
[docs] def fit(self, ins: FitIns) -> FitRes: """Refine the provided parameters using the locally held dataset. Parameters ---------- 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. """ _ = (self, ins) return FitRes( status=Status( code=Code.FIT_NOT_IMPLEMENTED, message="Client does not implement `fit`", ), parameters=Parameters(tensor_type="", tensors=[]), num_examples=0, metrics={}, )
[docs] def evaluate(self, ins: EvaluateIns) -> EvaluateRes: """Evaluate the provided parameters using the locally held dataset. Parameters ---------- 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. """ _ = (self, ins) return EvaluateRes( status=Status( code=Code.EVALUATE_NOT_IMPLEMENTED, message="Client does not implement `evaluate`", ), loss=0.0, num_examples=0, metrics={}, )
[docs] def get_context(self) -> Context: """Get the run context from this client.""" return self.context
[docs] def set_context(self, context: Context) -> None: """Apply a run context to this client.""" self.context = context
[docs] def to_client(self) -> Client: """Return client (itself).""" return self
def has_get_properties(client: Client) -> bool: """Check if Client implements get_properties.""" return type(client).get_properties != Client.get_properties def has_get_parameters(client: Client) -> bool: """Check if Client implements get_parameters.""" return type(client).get_parameters != Client.get_parameters def has_fit(client: Client) -> bool: """Check if Client implements fit.""" return type(client).fit != Client.fit def has_evaluate(client: Client) -> bool: """Check if Client implements evaluate.""" return type(client).evaluate != Client.evaluate def maybe_call_get_properties( client: Client, get_properties_ins: GetPropertiesIns ) -> GetPropertiesRes: """Call `get_properties` if the client overrides it.""" # Check if client overrides `get_properties` if not has_get_properties(client=client): # If client does not override `get_properties`, don't call it status = Status( code=Code.GET_PROPERTIES_NOT_IMPLEMENTED, message="Client does not implement `get_properties`", ) return GetPropertiesRes( status=status, properties={}, ) # If the client implements `get_properties`, call it return client.get_properties(get_properties_ins) def maybe_call_get_parameters( client: Client, get_parameters_ins: GetParametersIns ) -> GetParametersRes: """Call `get_parameters` if the client overrides it.""" # Check if client overrides `get_parameters` if not has_get_parameters(client=client): # If client does not override `get_parameters`, don't call it status = Status( code=Code.GET_PARAMETERS_NOT_IMPLEMENTED, message="Client does not implement `get_parameters`", ) return GetParametersRes( status=status, parameters=Parameters(tensor_type="", tensors=[]), ) # If the client implements `get_parameters`, call it return client.get_parameters(get_parameters_ins) def maybe_call_fit(client: Client, fit_ins: FitIns) -> FitRes: """Call `fit` if the client overrides it.""" # Check if client overrides `fit` if not has_fit(client=client): # If client does not override `fit`, don't call it status = Status( code=Code.FIT_NOT_IMPLEMENTED, message="Client does not implement `fit`", ) return FitRes( status=status, parameters=Parameters(tensor_type="", tensors=[]), num_examples=0, metrics={}, ) # If the client implements `fit`, call it return client.fit(fit_ins) def maybe_call_evaluate(client: Client, evaluate_ins: EvaluateIns) -> EvaluateRes: """Call `evaluate` if the client overrides it.""" # Check if client overrides `evaluate` if not has_evaluate(client=client): # If client does not override `evaluate`, don't call it status = Status( code=Code.EVALUATE_NOT_IMPLEMENTED, message="Client does not implement `evaluate`", ) return EvaluateRes( status=status, loss=0.0, num_examples=0, metrics={}, ) # If the client implements `evaluate`, call it return client.evaluate(evaluate_ins)