Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving-Reference-Cited by-同舟云学术

Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving

Published:2023-10-01 Issue: Volume: Page:
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Container-title:2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Author:

Kou Wei-Bin¹,Wang Shuai²,Zhu Guangxu³,Luo Bin⁴,Chen Yingxian¹,Kwan Ng Derrick Wing⁵,Wu Yik-Chung¹

Affiliation:

1. The University of Hong Kong,Department of Electrical and Electronic Engineering,Hong Kong,China,999077

2. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ),Shenzhen,China

3. Shenzhen Research Institute of Big Data,Shenzhen,China

4. Damo Institute,Alibaba,China

5. School of Electrical Engineering and Telecommunications, the University of New South Wales,Australia

Publisher

IEEE

Link

Reference33 articles.

1. Federated learning with domain generalization;Zhang,2021

2. Federated transfer reinforcement learning for autonomous driving;Liang,2019

3. Lifelong federated reinforcement learning: A learning architecture for navigation in cloud robotic systems;Liu,2019

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