信息安全研究2025，Vol.11Issue(5)：481-488,8.DOI:10.12379/j.issn.2096-1057.2025.05.11
电池监测的个性化联邦学习中与任务无关的隐私保护研究
Task Independent Privacy Protection in Personalized Federated Learning for Battery Monitoring
摘要
Abstract
For the health management of batteries in new energy vehicles,it is essential to collaboratively share distributed battery data and establish a federated learning model to extract valuable information.To counteract the privacy leakage risks associated with battery data sharing,this paper designs a task-independent privacy protection and communication-efficient federated learning-empowered edge intelligence model.This model learns personalized sub-networks that generalize well to local data and uses network pruning to find the optimal sub-network,ensuring inference accuracy.Meanwhile,to resist feature reconstruction attacks and privacy leakage risks,it constructs task-independent privacy-protective anonymous intermediate representations.By employing adversarial training,it maximizes the reconstruction error of the adversarial reconstructor and the classification error of the adversarial classifier,while minimizing the classification error of the target classifier.Experimental simulations show that this method improves inference accuracy by 8.85％and reduces communication overhead by 1.95 times.The balance analysis of utility and privacy demonstrates that it ensures the accuracy of target feature extraction while protecting privacy.关键词
隐私保护/联邦学习/特征提取/电池监测/网络剪枝Key words
privacy protection/federated learning/feature extraction/battery monitoring/network pruning分类
信息技术与安全科学引用本文复制引用
王睿涵,王勇..电池监测的个性化联邦学习中与任务无关的隐私保护研究[J].信息安全研究,2025,11(5):481-488,8.基金项目
陕西省重点研发计划项目(2024GX-YBXM-103) （2024GX-YBXM-103）
国家重点研发计划项目(2018YFB0804103) （2018YFB0804103）
