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FedAV:Federated learning for cyberattack vulnerability and resilience of cooperative driving automation

Guanyu Lin Sean Qian Zulqarnain H.Khattak

交通研究通讯（英文）2025，Vol.5Issue(2)：107-122,16.

交通研究通讯（英文）2025，Vol.5Issue(2)：107-122,16.DOI:10.1016/j.commtr.2025.100175

FedAV:Federated learning for cyberattack vulnerability and resilience of cooperative driving automation

Guanyu Lin ¹Sean Qian ¹Zulqarnain H.Khattak²

作者信息

1. Civil and Environmental Engineering,Carnegie Mellon University,Pittsburgh,PA,15213,USA
2. Transportation and Urban Infrastructure Studies,Morgan State University,Baltimore,MD,21251,USA
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摘要

关键词

Cybersecurity/Cyberattack/Connected automated vehicles/Anomaly detection/Federated learning/Agent/Vehicle to everything(V2X)/Vehicle platooning

Key words

Cybersecurity/Cyberattack/Connected automated vehicles/Anomaly detection/Federated learning/Agent/Vehicle to everything(V2X)/Vehicle platooning

引用本文复制引用

Guanyu Lin,Sean Qian,Zulqarnain H.Khattak..FedAV:Federated learning for cyberattack vulnerability and resilience of cooperative driving automation[J].交通研究通讯（英文）,2025,5(2):107-122,16.

基金项目

This work was sponsored by TraCR University Transportation Center,sponsored by the United States Department of Transportation.The work also received support from Safety21 University Transportation Center,funded by the United States Department of Transportation.The authors gratefully acknowledge this support. （）

交通研究通讯（英文）

ISSN：2772-4247

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