面向联邦学习的可验证安全聚合方案  

Verifiable and Secure Aggregation Scheme for Federated Learning

作　　者：任艳丽[1] 付燕霞 李烨榕 REN Yanli;FU Yanxia;LI Yerong(School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学通信与信息工程学院,上海200444

出　　处：《北京邮电大学学报》2023年第3期49-55,共7页Journal of Beijing University of Posts and Telecommunications

基　　金：上海市科技计划项目(20ZR1419700)。

摘　　要：在联邦学习中,多个数据拥有者可以联合训练一个高质量模型,有效地解决了数据孤岛问题,且能实现用户数据的隐私保护。然而,目前的联邦学习存在模型泄露、训练结果无法验证以及用户计算和通信代价较高等问题。对此,提出了面向联邦学习的隐私增强可验证安全聚合方案,实现了用户数据和模型参数的隐私保护,训练结果的可验证性,且大幅降低了用户的计算开销和通信代价。所提方案采用同态加密算法处理浮点运算,基于线性同态哈希函数验证聚合结果的正确性,其中部分用户掉线不影响最终的聚合结果。实验结果表明,所提方案具有较小的计算开销,且有效提高了训练模型的检测性能。During the federated learning,multiple data owners can jointly train a high⁃quality model,which effectively solves the problem of data silos and protects the privacy of the user data.However,the current federated learning has problems such as model leakage,unverifiable training results,high user computing and communication costs.To solve the above problems,a privacy⁃enhanced and verifiable security aggregation scheme for federated learning is proposed,which simultaneously realizes the privacy protection of user data and model parameters,and the verifiability of training results.The proposed scheme greatly reduces the computational and communication overhead of users.The scheme uses the homomorphic encryption algorithm to process floating⁃point operations,and verifies the correctness of the aggregation results based on a linear homomorphic hash function.Even if some users are offline,the final aggregation results will not be affected.The experimental results show that this scheme has less computational overhead and effectively improves the test performance of the trained model.

关 键 词：联邦学习 同态加密 安全聚合 可验证 隐私增强 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]