# 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 server strategy."""
from abc import ABC, abstractmethod
from typing import Optional, Union
from flwr.common import EvaluateIns, EvaluateRes, FitIns, FitRes, Parameters, Scalar
from flwr.server.client_manager import ClientManager
from flwr.server.client_proxy import ClientProxy
[docs]
class Strategy(ABC):
"""Abstract base class for server strategy implementations."""
[docs]
@abstractmethod
def initialize_parameters(
self, client_manager: ClientManager
) -> Optional[Parameters]:
"""Initialize the (global) model parameters.
Parameters
----------
client_manager : ClientManager
The client manager which holds all currently connected clients.
Returns
-------
parameters : Optional[Parameters]
If parameters are returned, then the server will treat these as the
initial global model parameters.
"""
[docs]
@abstractmethod
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 training results.
Parameters
----------
server_round : int
The current round of federated learning.
results : List[Tuple[ClientProxy, FitRes]]
Successful updates from the previously selected and configured
clients. Each pair of `(ClientProxy, FitRes)` constitutes a
successful update from one of the previously selected clients. Not
that not all previously selected clients are necessarily included in
this list: a client might drop out and not submit a result. For each
client that did not submit an update, there should be an `Exception`
in `failures`.
failures : List[Union[Tuple[ClientProxy, FitRes], BaseException]]
Exceptions that occurred while the server was waiting for client
updates.
Returns
-------
parameters : Tuple[Optional[Parameters], Dict[str, Scalar]]
If parameters are returned, then the server will treat these as the
new global model parameters (i.e., it will replace the previous
parameters with the ones returned from this method). If `None` is
returned (e.g., because there were only failures and no viable
results) then the server will no update the previous model
parameters, the updates received in this round are discarded, and
the global model parameters remain the same.
"""
[docs]
@abstractmethod
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 results.
Parameters
----------
server_round : int
The current round of federated learning.
results : List[Tuple[ClientProxy, FitRes]]
Successful updates from the
previously selected and configured clients. Each pair of
`(ClientProxy, FitRes` constitutes a successful update from one of the
previously selected clients. Not that not all previously selected
clients are necessarily included in this list: a client might drop out
and not submit a result. For each client that did not submit an update,
there should be an `Exception` in `failures`.
failures : List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]]
Exceptions that occurred while the server was waiting for client updates.
Returns
-------
aggregation_result : Tuple[Optional[float], Dict[str, Scalar]]
The aggregated evaluation result. Aggregation typically uses some variant
of a weighted average.
"""
[docs]
@abstractmethod
def evaluate(
self, server_round: int, parameters: Parameters
) -> Optional[tuple[float, dict[str, Scalar]]]:
"""Evaluate the current model parameters.
This function can be used to perform centralized (i.e., server-side) evaluation
of model parameters.
Parameters
----------
server_round : int
The current round of federated learning.
parameters: Parameters
The current (global) model parameters.
Returns
-------
evaluation_result : Optional[Tuple[float, Dict[str, Scalar]]]
The evaluation result, usually a Tuple containing loss and a
dictionary containing task-specific metrics (e.g., accuracy).
"""