计算机应用研究2026，Vol.43Issue(5)：1511-1519,9.DOI:10.19734/j.issn.1001-3695.2025.08.0375
HEAFed:同态加密的高效通信异步联邦学习算法
HEAFed:homomorphic encryption-based communication-efficient asynchronous federated learning algorithm
摘要
Abstract
Federated learning systems face risks of privacy data theft by attackers or semi-honest servers,and also suffer from low accuracy and inefficiency in unstable communication environments.To address these issues,this paper proposed an effi-cient communication asynchronous federated learning algorithm based on homomorphic encryption,named HEAFed.The algo-rithm firstly utilized the Chinese remainder theorem(CRT)to compress client parameter updates.It then employed an im-proved Paillier algorithm to encrypt the optimized parameter updates,ensuring user privacy security.Furthermore,it intro-duced an asynchronous aggregation mechanism with parameter adjustment,effectively incorporating the training outcomes of delayed clients.Experiments were conducted on the MNIST and CIFAR-10 datasets.Results show that HEAFed outperforms traditional privacy-preserving federated learning methods in both accuracy and efficiency.It performs especially well in unsta-ble communication environments.Compared with four baseline algorithms,HEAFed improves accuracy by 12.59％to 43.09％.关键词
联邦学习/隐私保护/同态加密/中国剩余定理Key words
federated learning/privacy protection/homomorphic encryption/Chinese remainder theorem分类
信息技术与安全科学引用本文复制引用
王斌,郑兵,陈运..HEAFed:同态加密的高效通信异步联邦学习算法[J].计算机应用研究,2026,43(5):1511-1519,9.基金项目
黑龙江省高等学校基本科研业务费优秀创新团队建设项目(2023-KYYWF-0639) （2023-KYYWF-0639）
佳木斯大学博士专项科研基金启动项目(JMSUBZ2022-12) （JMSUBZ2022-12）
