计算机工程与应用2024，Vol.60Issue(14)：294-305,12.DOI:10.3778/j.issn.1002-8331.2304-0287
横向联邦学习系统的安全聚合方法
Secure Aggregation Scheme for Horizontal Fedetated Learning System
摘要
Abstract
A secure model aggregation scheme for privacy-preserving horizontal federated learning system is proposed in this paper.When homomorphic encryption is used in a horizontal federated learning system for privacy protection,the aggregation server can accurately detect Byzantine participants and realize the secure aggregation of the global model.The experimental results show that the proposed scheme can obtain a global model with high accuracy when there are Byzantine participants in the system,and will not bring too much computational and communication overhead to the feder-ated learning system.关键词
联邦学习/安全聚合/拜占庭攻击/异常检测/隐私保护Key words
federated learning/secure aggregation/Byzantine attack/anomaly detection/privacy preserving分类
信息技术与安全科学引用本文复制引用
黄秀丽,于鹏飞,高先周..横向联邦学习系统的安全聚合方法[J].计算机工程与应用,2024,60(14):294-305,12.基金项目
国家电网有限公司总部管理科技项目(5700-202190184A-0-0-00). （5700-202190184A-0-0-00）
