Collaborative Learning on the Edges: A Case Study on Connected Vehicles

Authors: 

Sidi Lu, Yongtao Yao, and Weisong Shi, Wayne State University

Abstract: 

The wide deployment of 4G/5G has enabled connected vehicles as a perfect edge computing platform for a plethora of new services which are impossible before, such as remote real-time diagnostics and advanced driver assistance. In this work, we propose CLONE, a collaborative learning setting on the edges based on the real-world dataset collected from a large electric vehicle (EV) company. Our approach is built on top of the federated learning algorithm and long short-term memory networks, and it demonstrates the effectiveness of driver personalization, privacy serving, latency reduction (asynchronous execution), and security protection. We choose the failure of EV battery and associated accessories as our case study to show how the CLONE solution can accurately predict failures to ensure sustainable and reliable driving in a collaborative fashion.

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BibTeX
@inproceedings {234807,
author = {Sidi Lu and Yongtao Yao and Weisong Shi},
title = {Collaborative Learning on the Edges: A Case Study on Connected Vehicles},
booktitle = {2nd {USENIX} Workshop on Hot Topics in Edge Computing (HotEdge 19)},
year = {2019},
address = {Renton, WA},
url = {https://www.usenix.org/conference/hotedge19/presentation/lu},
publisher = {{USENIX} Association},
month = jul,
}