物联网学报2024，Vol.8Issue(4)：14-22,9.DOI:10.11959/j.issn.2096-3750.2024.00452
车辆算力网络中异步鲁棒联邦学习方法研究
Research on asynchronous robust federated learning method in vehicle computing power network
摘要
Abstract
The synchronous training mechanism of traditional federated learning was not suitable for dynamic vehicle computing power network scenarios,and lacked effective detection mechanisms under the threat of malicious vehicle at-tacks.To address the above issues,an asynchronous robust federated learning method was proposed,which achieves ve-hicle data privacy protection while improving the efficiency of model collaborative training through asynchronous execu-tion of federated learning processes between vehicles.Secondly,a model selection method was designed,and potential malicious model detection and vehicle reputation evaluation methods are proposed to further enhance the robustness of the system.Then,the safety of the proposed method was analyzed in detail from a probabilistic perspective,providing a theoretical basis for optimizing various parameters.Finally,the simulation results show that this method can achieve effi-cient asynchronous federated learning while having good robustness.关键词
车辆算力网络/联邦学习/鲁棒性/异步学习Key words
vehicle computing power network/federated learning/robustness/asynchronous learning分类
信息技术与安全科学引用本文复制引用
尹宏博,王帅,张科,张引..车辆算力网络中异步鲁棒联邦学习方法研究[J].物联网学报,2024,8(4):14-22,9.基金项目
广东省重点研发计划(No.2024B1111060001)The Key Research and Development Program of Guangdong Province(No.2024B1111060001) （No.2024B1111060001）
