湖南大学学报（自然科学版）2026，Vol.53Issue(6)：99-110,12.DOI:10.16339/j.cnki.hdxbzkb.2026275
面向高动态车载网的可验证联邦学习方案
A verifiable federated learning scheme for highly dynamic vehicular ad-hoc networks
摘要
Abstract
With the rapid development of intelligent transportation systems,federated learning(FL)has made remarkable progress in vehicular ad-hoc networks(VANETs),establishing itself as a crucial approach to improve data sharing efficiency and privacy protection.However,the existing solutions face significant challenges arising from the dynamic nature of the networks,the rapid mobility of vehicles,and frequent environmental changes,particularly in terms of privacy preservation,malicious vehicle management,verification mechanisms,and so on.To address these challenges,a novel and verifiable FL scheme for VANETs is proposed in this paper.First,an enhanced Paillier homomorphic encryption algorithm is integrated into the framework,which not only strengthens privacy protection but also reduces computational overhead,thus meeting the frequent data processing requirements of VANETs.Second,a malicious vehicle revocation and tracking mechanism,based on group keys and the cosine similarity,is introduced to manage the dynamic joining and leaving of malicious vehicles,thereby enhancing the overall security of the system.Finally,a lightweight aggregate signature verification mechanism is implemented using elliptic curve-based certificate-free signatures,effectively mitigating the high overhead typically associated with traditional certificate management while reducing the storage and communication burdens on vehicles,thereby improving the efficiency of message authentication.Meanwhile,the communication environment and vehicle dynamic information processing mechanism are optimized to enhance the scheme's adaptability to the highly dynamic VANET environment.Experimental results demonstrate that the proposed scheme significantly outperforms the existing methods in terms of both privacy protection and computational efficiency,offering a practical solution for the deployment of FL in vehicular network environments.关键词
联邦学习/同态加密/车载网/群组密钥/无证书签名Key words
federated learning/homomorphic encryption/vehicular networks/group key/certificateless signa-tures分类
信息技术与安全科学引用本文复制引用
李亚红,李一婧,杨小东,张源,牛淑芬..面向高动态车载网的可验证联邦学习方案[J].湖南大学学报（自然科学版）,2026,53(6):99-110,12.基金项目
国家自然科学基金资助项目(62461032),National Natural Science Foundation of China(62461032) （62461032）
甘肃省科技计划(22JR5RA158,22JR5RA350),Gansu Province Science and Technology Plan(22JR5RA158,22JR5RA350) （22JR5RA158,22JR5RA350）
甘肃省高校教师创新基金项目(2023A-041,2023-ZD-234),Gansu Province University Teachers Innovation Fund Project(2023A-041,2023-ZD-234) （2023A-041,2023-ZD-234）
兰州交通大学-天津大学联合创新基金(LH2024003),Lanzhou Jiaotong University-Tianjin University Joint Innovation Fund Project(LH2024003) （LH2024003）
甘肃省教育科技创新项目(2026B-266),Gansu Provincial Education,Science and Technology Innovation Project(2026B-266) （2026B-266）
