# Copyright (c) 2025, NVIDIA CORPORATION. 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.
import numpy as np
from nvflare.apis.dxo import DXO, DataKind, from_shareable
from nvflare.apis.fl_context import FLContext
from nvflare.apis.shareable import Shareable
from nvflare.app_common.abstract.aggregator import Aggregator
[docs]
class ModelUpdateDXOAggregator(Aggregator):
def __init__(self):
Aggregator.__init__(self)
self.dict = None
self.count = 0
def _aggregate(self, weight_base, weight_to_add):
# aggregates the dict on corresponding keys
for key, sub_object in weight_base.items():
if isinstance(sub_object, dict):
sub_to_add = weight_to_add.get(key)
self._aggregate(sub_object, sub_to_add)
weight_base[key] = np.add(weight_base[key], weight_to_add[key])
return weight_base
[docs]
def reset(self, fl_ctx: FLContext):
self.dict = None
self.count = 0
[docs]
def accept(self, shareable: Shareable, fl_ctx: FLContext) -> bool:
dxo = from_shareable(shareable)
# check data_kind
if dxo.data_kind != DataKind.WEIGHT_DIFF:
raise ValueError(f"DXO data_kind must be {DataKind.WEIGHT_DIFF}, but got {dxo.data_kind}")
# get weights and add to base
weight_to_add = dxo.data.get("dict")
# convert to numpy arrays if they are lists
if weight_to_add is not None:
for key, value in weight_to_add.items():
if isinstance(value, list):
weight_to_add[key] = np.array(value)
if weight_to_add is None:
raise ValueError("Model dict is empty, please check the message")
if self.dict is None:
self.dict = weight_to_add
else:
self.dict = self._aggregate(self.dict, weight_to_add)
# get count and add to base
count = dxo.data.get("count", 1)
# print the count
self.log_info(fl_ctx, f"Aggregator got {count} updates")
self.count += count
return True
[docs]
def aggregate(self, fl_ctx: FLContext) -> Shareable:
dxo = DXO(data_kind=DataKind.WEIGHT_DIFF, data={"dict": self.dict, "count": self.count})
# once returned to upper layer, reset the aggregator
self.reset(fl_ctx)
return dxo.to_shareable()