# Source code for nvflare.app_common.aggregators.weighted_aggregation_helper

```
# Copyright (c) 2022, 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 re
import threading
from typing import Optional
[docs]class WeightedAggregationHelper(object):
def __init__(self, exclude_vars: Optional[str] = None, weigh_by_local_iter: bool = True):
"""Perform weighted aggregation.
Args:
exclude_vars (str, optional): regex string to match excluded vars during aggregation. Defaults to None.
weigh_by_local_iter (bool, optional): Whether to weight the contributions by the number of iterations
performed in local training in the current round. Defaults to `True`.
Setting it to `False` can be useful in applications such as homomorphic encryption to reduce
the number of computations on encrypted ciphertext.
The aggregated sum will still be divided by the provided weights and `aggregation_weights` for the
resulting weighted sum to be valid.
"""
super().__init__()
self.lock = threading.Lock()
self.exclude_vars = re.compile(exclude_vars) if exclude_vars else None
self.weigh_by_local_iter = weigh_by_local_iter
self.reset_stats()
self.total = dict()
self.counts = dict()
self.history = list()
[docs] def add(self, data, weight, contributor_name, contribution_round):
"""Compute weighted sum and sum of weights."""
with self.lock:
for k, v in data.items():
if self.exclude_vars is not None and self.exclude_vars.search(k):
continue
if self.weigh_by_local_iter:
weighted_value = v * weight
else:
weighted_value = v # used in homomorphic encryption to reduce computations on ciphertext
current_total = self.total.get(k, None)
if current_total is None:
self.total[k] = weighted_value
self.counts[k] = weight
else:
self.total[k] = current_total + weighted_value
self.counts[k] = self.counts[k] + weight
self.history.append(
{
"contributor_name": contributor_name,
"round": contribution_round,
"weight": weight,
}
)
[docs] def get_result(self):
"""Divide weighted sum by sum of weights."""
with self.lock:
aggregated_dict = {k: v * (1.0 / self.counts[k]) for k, v in self.total.items()}
self.reset_stats()
return aggregated_dict
```