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Federated Intelligence for Intelligent Vehicles

Authors :
Zhang, Weishan
Zhang, Baoyu
Jia, Xiaofeng
Qi, Hongwei
Qin, Rui
Li, Juanjuan
Tian, Yonglin
Liang, Xiaolong
Wang, Fei-Yue
Source :
IEEE Transactions on Intelligent Vehicles; 2024, Vol. 9 Issue: 5 p4835-4839, 5p
Publication Year :
2024

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.

Details

Language :
English
ISSN :
23798858
Volume :
9
Issue :
5
Database :
Supplemental Index
Journal :
IEEE Transactions on Intelligent Vehicles
Publication Type :
Periodical
Accession number :
ejs67054459
Full Text :
https://doi.org/10.1109/TIV.2024.3415410