Federated Intelligence for Intelligent Vehicles

IF 14 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Weishan Zhang;Baoyu Zhang;Xiaofeng Jia;Hongwei Qi;Rui Qin;Juanjuan Li;Yonglin Tian;Xiaolong Liang;Fei-Yue Wang
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引用次数: 0

Abstract

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter.
智能车辆的联合智能
这封信是 IEEE TIV 关于智能车辆联盟智能的一系列分散和混合研讨会(DHWs)的简要总结。讨论结果如下1) 不同规模的大型模型(LM)可以联合部署在智能车身上,智能车可以采用三种大型模型和小型模型之间的联合协作。2) 联合微调 LMs 有利于 IVs 的数据安全。3) 利用联合智能优化现有模型并不断学习,可以提高 IV 的可持续性。4) LM 增强的知识可以使 IVs 更加智能。
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来源期刊
IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Mathematics-Control and Optimization
CiteScore
12.10
自引率
13.40%
发文量
177
期刊介绍: The IEEE Transactions on Intelligent Vehicles (T-IV) is a premier platform for publishing peer-reviewed articles that present innovative research concepts, application results, significant theoretical findings, and application case studies in the field of intelligent vehicles. With a particular emphasis on automated vehicles within roadway environments, T-IV aims to raise awareness of pressing research and application challenges. Our focus is on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and others interested in learning about the state-of-the-art developments and progress in research and applications related to intelligent vehicles. Join us in advancing knowledge and innovation in this dynamic field.
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