测试技术学报2025，Vol.39Issue(3)：291-297,304,8.DOI:10.62756/csjs.1671-7449.2025036
基于区块链声誉管理的安全公平的联邦学习
Secure and Fair Federated Leaning Based on Blockchain Reputation Management
范志强 1张志才2
作者信息
- 1. 山西大学 物理电子工程学院,山西 太原 030006
- 2. 海南大学 计算机科学与技术学院,海南 海口 570228
- 折叠
摘要
Abstract
Federated learning(FL),as a secure distributed learning framework,has attracted widespread interest in the field of the Internet of Things(IoT)because it can effectively protect the data privacy of participants.However,the traditional FL architecture is,to some extent,centralized and requires a cen-tral server to be responsible for model updates and aggregation.This centralized structure is susceptible to single-point attacks,which may cause the entire FL system to crash,and is also vulnerable to attacks from free riders,thereby affecting fairness and safety.To address these challenges,a completely distrib-uted structure that is different from traditional centralized management structures is proposed.The local model from clients is decentralized and managed through a consortium blockchain with undeniable and tamper-resistant features.In addition,the reputation evaluation for clients is introduced in the block-chain to prevent attacks from hitchhikers,and honest participants with different rewards are rewarded based on different reputation values.The experiment results show that this method can achieve high fairness and identify and exclude free riders effectively.关键词
联邦学习/搭便车攻击/区块链/声誉评估Key words
federated learning/hitchhiking attacks/blockchain/reputation assessment分类
计算机与自动化引用本文复制引用
范志强,张志才..基于区块链声誉管理的安全公平的联邦学习[J].测试技术学报,2025,39(3):291-297,304,8.
