# Copyright 2025 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 message-based strategy."""
import io
import time
from abc import ABC, abstractmethod
from collections.abc import Iterable
from logging import INFO
from typing import Callable, Optional
from flwr.common import ArrayRecord, ConfigRecord, Message, MetricRecord, log
from flwr.server import Grid
from .result import Result
from .strategy_utils import log_strategy_start_info
[docs]
class Strategy(ABC):
"""Abstract base class for server strategy implementations."""
[docs]
@abstractmethod
def aggregate_train(
self,
server_round: int,
replies: Iterable[Message],
) -> tuple[Optional[ArrayRecord], Optional[MetricRecord]]:
"""Aggregate training results from client nodes.
Parameters
----------
server_round : int
The current round of federated learning, starting from 1.
replies : Iterable[Message]
Iterable of reply messages received from client nodes after training.
Each message contains ArrayRecords and MetricRecords that get aggregated.
Returns
-------
tuple[Optional[ArrayRecord], Optional[MetricRecord]]
A tuple containing:
- ArrayRecord: Aggregated ArrayRecord, or None if aggregation failed
- MetricRecord: Aggregated MetricRecord, or None if aggregation failed
"""
[docs]
@abstractmethod
def aggregate_evaluate(
self,
server_round: int,
replies: Iterable[Message],
) -> Optional[MetricRecord]:
"""Aggregate evaluation metrics from client nodes.
Parameters
----------
server_round : int
The current round of federated learning.
replies : Iterable[Message]
Iterable of reply messages received from client nodes after evaluation.
MetricRecords in the messages are aggregated.
Returns
-------
Optional[MetricRecord]
Aggregated evaluation metrics from all participating clients,
or None if aggregation failed.
"""
[docs]
@abstractmethod
def summary(self) -> None:
"""Log summary configuration of the strategy."""
# pylint: disable=too-many-arguments, too-many-positional-arguments, too-many-locals
[docs]
def start(
self,
grid: Grid,
initial_arrays: ArrayRecord,
num_rounds: int = 3,
timeout: float = 3600,
train_config: Optional[ConfigRecord] = None,
evaluate_config: Optional[ConfigRecord] = None,
evaluate_fn: Optional[
Callable[[int, ArrayRecord], Optional[MetricRecord]]
] = None,
) -> Result:
"""Execute the federated learning strategy.
Runs the complete federated learning workflow for the specified number of
rounds, including training, evaluation, and optional centralized evaluation.
Parameters
----------
grid : Grid
The Grid instance used to send/receive Messages from nodes executing a
ClientApp.
initial_arrays : ArrayRecord
Initial model parameters (arrays) to be used for federated learning.
num_rounds : int (default: 3)
Number of federated learning rounds to execute.
timeout : float (default: 3600)
Timeout in seconds for waiting for node responses.
train_config : ConfigRecord, optional
Configuration to be sent to nodes during training rounds.
If unset, an empty ConfigRecord will be used.
evaluate_config : ConfigRecord, optional
Configuration to be sent to nodes during evaluation rounds.
If unset, an empty ConfigRecord will be used.
evaluate_fn : Callable[[int, ArrayRecord], Optional[MetricRecord]], optional
Optional function for centralized evaluation of the global model. Takes
server round number and array record, returns a MetricRecord or None. If
provided, will be called before the first round and after each round.
Defaults to None.
Returns
-------
Results
Results containing final model arrays and also training metrics, evaluation
metrics and global evaluation metrics (if provided) from all rounds.
"""
log(INFO, "Starting %s strategy:", self.__class__.__name__)
log_strategy_start_info(
num_rounds, initial_arrays, train_config, evaluate_config
)
self.summary()
log(INFO, "")
# Initialize if None
train_config = ConfigRecord() if train_config is None else train_config
evaluate_config = ConfigRecord() if evaluate_config is None else evaluate_config
result = Result()
t_start = time.time()
# Evaluate starting global parameters
if evaluate_fn:
res = evaluate_fn(0, initial_arrays)
log(INFO, "Initial global evaluation results: %s", res)
if res is not None:
result.evaluate_metrics_serverapp[0] = res
arrays = initial_arrays
for current_round in range(1, num_rounds + 1):
log(INFO, "")
log(INFO, "[ROUND %s/%s]", current_round, num_rounds)
# -----------------------------------------------------------------
# --- TRAINING (CLIENTAPP-SIDE) -----------------------------------
# -----------------------------------------------------------------
# Call strategy to configure training round
# Send messages and wait for replies
train_replies = grid.send_and_receive(
messages=self.configure_train(
current_round,
arrays,
train_config,
grid,
),
timeout=timeout,
)
# Aggregate train
agg_arrays, agg_train_metrics = self.aggregate_train(
current_round,
train_replies,
)
# Log training metrics and append to history
if agg_arrays is not None:
result.arrays = agg_arrays
arrays = agg_arrays
if agg_train_metrics is not None:
log(INFO, "\t└──> Aggregated MetricRecord: %s", agg_train_metrics)
result.train_metrics_clientapp[current_round] = agg_train_metrics
# -----------------------------------------------------------------
# --- EVALUATION (CLIENTAPP-SIDE) ---------------------------------
# -----------------------------------------------------------------
# Call strategy to configure evaluation round
# Send messages and wait for replies
evaluate_replies = grid.send_and_receive(
messages=self.configure_evaluate(
current_round,
arrays,
evaluate_config,
grid,
),
timeout=timeout,
)
# Aggregate evaluate
agg_evaluate_metrics = self.aggregate_evaluate(
current_round,
evaluate_replies,
)
# Log training metrics and append to history
if agg_evaluate_metrics is not None:
log(INFO, "\t└──> Aggregated MetricRecord: %s", agg_evaluate_metrics)
result.evaluate_metrics_clientapp[current_round] = agg_evaluate_metrics
# -----------------------------------------------------------------
# --- EVALUATION (SERVERAPP-SIDE) ---------------------------------
# -----------------------------------------------------------------
# Centralized evaluation
if evaluate_fn:
log(INFO, "Global evaluation")
res = evaluate_fn(current_round, arrays)
log(INFO, "\t└──> MetricRecord: %s", res)
if res is not None:
result.evaluate_metrics_serverapp[current_round] = res
log(INFO, "")
log(INFO, "Strategy execution finished in %.2fs", time.time() - t_start)
log(INFO, "")
log(INFO, "Final results:")
log(INFO, "")
for line in io.StringIO(str(result)):
log(INFO, "\t%s", line.strip("\n"))
log(INFO, "")
return result