Blockchain-based Node-aware Dynamic Weighting Methods for Improving Federated Learning Performance

You Jun Kim, Choong Seon Hong

doi:10.34385/proc.56.p3-1

Federated learning (FL) is a decentralized learning method that deviated from the conventional centralized learning. The FL progresses learning locally on each device and gradually improves the learning model through interaction with the central server. If the FL is applied to blockchain network, it can get many advantages such as security, integrity and efficient incentive system. However, it can cause network overload because of limited communication bandwidth and the participation of a huge number of users. Furthermore, learning speed slow down because of blockchain network. One of the ways to minimize the network load and improve the learning speed are for the model to converge rapidly and stably with target learning accuracy. In this paper, we propose blockchain based federated learning scenario. We will explain of some benefits for blockchain and some methods for improving learning performance. We consider two types of weights to choose the subset of clients for updating the global model. First, we consider the weight based on local learning accuracy of each client. Second, we consider the weight based on participation frequency of each client. We choose two key performance indicators, learning speed and standard deviation, to compare the performance of our proposed scheme with existing schemes. The simulation results show that our proposed scheme achieves higher stability along with fast convergence time for targeted accuracy compared to others.

Blockchain-based Node-aware Dynamic Weighting Methods for Improving Federated Learning Performance

抄録

収録刊行物

キーワード

詳細情報詳細情報について

書き出し

問題の指摘

Blockchain-based Node-aware Dynamic Weighting Methods for Improving Federated Learning Performance

抄録

収録刊行物

キーワード

詳細情報 詳細情報について

書き出し

問題の指摘

参加プロジェクトリスト

詳細情報詳細情報について