# Copyright 2023 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.
# ==============================================================================
"""Federated XGBoost bagging aggregation strategy."""
import json
from logging import WARNING
from typing import Any, Callable, Optional, Union, cast
from flwr.common import EvaluateRes, FitRes, Parameters, Scalar
from flwr.common.logger import log
from flwr.server.client_proxy import ClientProxy
from .fedavg import FedAvg
[docs]
class FedXgbBagging(FedAvg):
"""Configurable FedXgbBagging strategy implementation."""
# pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long
def __init__(
self,
evaluate_function: Optional[
Callable[
[int, Parameters, dict[str, Scalar]],
Optional[tuple[float, dict[str, Scalar]]],
]
] = None,
**kwargs: Any,
):
self.evaluate_function = evaluate_function
self.global_model: Optional[bytes] = None
super().__init__(**kwargs)
def __repr__(self) -> str:
"""Compute a string representation of the strategy."""
rep = f"FedXgbBagging(accept_failures={self.accept_failures})"
return rep
[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 bagging."""
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 all the client trees
global_model = self.global_model
for _, fit_res in results:
update = fit_res.parameters.tensors
for bst in update:
global_model = aggregate(global_model, bst)
self.global_model = global_model
return (
Parameters(tensor_type="", tensors=[cast(bytes, global_model)]),
{},
)
[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 metrics using 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 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 0, metrics_aggregated
[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_function is None:
# No evaluation function provided
return None
eval_res = self.evaluate_function(server_round, parameters, {})
if eval_res is None:
return None
loss, metrics = eval_res
return loss, metrics
def aggregate(
bst_prev_org: Optional[bytes],
bst_curr_org: bytes,
) -> bytes:
"""Conduct bagging aggregation for given trees."""
if not bst_prev_org:
return bst_curr_org
# Get the tree numbers
tree_num_prev, _ = _get_tree_nums(bst_prev_org)
_, paral_tree_num_curr = _get_tree_nums(bst_curr_org)
bst_prev = json.loads(bytearray(bst_prev_org))
bst_curr = json.loads(bytearray(bst_curr_org))
bst_prev["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
"num_trees"
] = str(tree_num_prev + paral_tree_num_curr)
iteration_indptr = bst_prev["learner"]["gradient_booster"]["model"][
"iteration_indptr"
]
bst_prev["learner"]["gradient_booster"]["model"]["iteration_indptr"].append(
iteration_indptr[-1] + paral_tree_num_curr
)
# Aggregate new trees
trees_curr = bst_curr["learner"]["gradient_booster"]["model"]["trees"]
for tree_count in range(paral_tree_num_curr):
trees_curr[tree_count]["id"] = tree_num_prev + tree_count
bst_prev["learner"]["gradient_booster"]["model"]["trees"].append(
trees_curr[tree_count]
)
bst_prev["learner"]["gradient_booster"]["model"]["tree_info"].append(0)
bst_prev_bytes = bytes(json.dumps(bst_prev), "utf-8")
return bst_prev_bytes
def _get_tree_nums(xgb_model_org: bytes) -> tuple[int, int]:
xgb_model = json.loads(bytearray(xgb_model_org))
# Get the number of trees
tree_num = int(
xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
"num_trees"
]
)
# Get the number of parallel trees
paral_tree_num = int(
xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
"num_parallel_tree"
]
)
return tree_num, paral_tree_num