Mobility Aware Privacy-Preserving Federated Learning with Hierarchical Aggregation for Internet of Vehicles

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Mobility Aware Privacy-Preserving Federated Learning with Hierarchical Aggregation for Internet of Vehicles 44 Pages Posted: 14 Nov 2022 See all articles by Xing ChangXing ChangTongji UniversityXiaoping XueTongji UniversityJingxiao MaTongji UniversityYantao YuTongji UniversityTao SongTongji University Abstract With the application and development of intelligence and networking in the automobile industry, the Internet-of-Vehicles (IoV) can be empowered with collaborative and distributed intelligence using massive and geographically dispersed data, e.g., location and trajectory data, which have considerable privacy risks due to the privacy-sensitive nature of user data. Federated Learning (FL) has been regarded as an effective technique to preserve privacy for various intelligent applications in the IoV. However, when FL is applied in the IoV, the mobility nature of IoV brings specific challenges in privacy preservation. In this paper, we propose a mobility-aware privacy-preserving FL with hierarchical aggregation scheme to efficiently reduce the risk of privacy leakage and to enhance the privacy-preserving strength of FL, through a comprehensive resource scheduling design that optimizes communication overhead and learning efficiency under certifiable user-level privacy constraints. First, we formulate a vehicle selection optimization problem by comprehensively considering some important factors related to mobility and heterogeneity, such as vehicle location, vehicle speed, limited communication resources, and data heterogeneity, and a dynamic adaptive vehicle selection algorithm is proposed to dynamically select the suitable vehicles to participate in the learning task to improve the training efficiency of FL. Further, we provide the theoretical optimal noise addition algorithm and carry out a complete theoretical derivation, where local model updates are perturbed with additive Gaussian noise. Extensive experiments demonstrate that the proposed scheme can effectively improve the learning effectiveness of FL to reduce the risk of privacy leakage and improve the privacy preserving strength of the FL, thereby improving the ability to protect privacy in the IoV. Keywords: Federated Learning, Internet of vehicles, Participant Selection, Privacy-Preserving, Hierarchical Aggregation Suggested Citation: Suggested Citation Chang, Xing and Xue, Xiaoping and Ma, Jingxiao and Yu, Yantao and Song, Tao, Mobility Aware Privacy-Preserving Federated Learning with Hierarchical Aggregation for Internet of Vehicles. Available at SSRN: https://ssrn.com/abstract=4276311 Xing Chang Tongji University ( email ) ShanghaiChina Xiaoping Xue Tongji University ( email ) ShanghaiChina Jingxiao Ma (Contact Author) Tongji University ( email ) ShanghaiChina Yantao Yu Tongji University ( email ) ShanghaiChina Tao Song Tongji University ( email ) ShanghaiChina Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 1 Abstract Views 3 PlumX Metrics Related eJournals Data Science & Analytics eJournal Follow Data Science & Analytics eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 94 PAPERS 2,142 Information Policy & Ethics eJournal Follow Information Policy & Ethics eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 57 PAPERS 1,817 Libraries & Information Technology eJournal Follow Libraries & Information Technology eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 45 PAPERS 3,855 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo-4dc in 0.224 seconds