Road Supervised Federated Learning with Bug-Aware Sensor Placement

Chen, Jianjun; Wang, Shuai; Liu, Chenguang; Ng, Derrick Wing Kwan; Xu, Chengzhong; Hao, Qi; Lu, Haiyan

Abstract

Federated learning (FL) emerges as a promising solution to enhance autonomous driving (AD) models against out-of-distribution (OOD) data. However, OOD instances often lack labels, rendering conventional FL approaches less effective in AD. This paper proposes road-supervised FL (RSFL), which leverages road sensors' perception results to annotate vehicle sensors' data, providing a fresh perspective on data annotations for FLAD systems. To get deeper insights into RSFL, the information gain of annotating objects with road sensors is derived by leveraging the expected entropy reduction. Furthermore, a bug-aware sensor placement (BASP) algorithm is developed which strategically reduces (increases) the number of sensors in low (high) complexity scenarios. This is in contrast to traditional sensor placements where sensing coverage or road topology is the only consideration. It is shown that BASP approximately maximizes the information gain brought by road supervision. Experiments confirm the superiority of the proposed RSFL framework and BASP algorithm.

Citation

Chen, J., Wang, S., Liu, C., Ng, D. W. K., Xu, C., Hao, Q., & Lu, H. (online). Road Supervised Federated Learning with Bug-Aware Sensor Placement. IEEE Transactions on Vehicular Technology, 1-6. https://doi.org/10.1109/tvt.2024.3439105

• Journal Article Type: Article
• Acceptance Date: Jul 23, 2024
• Online Publication Date: Aug 6, 2024
• Deposit Date: Aug 20, 2024
• Publicly Available Date: Aug 21, 2024
• Journal: IEEE Transactions on Vehicular Technology
• Print ISSN: 0018-9545
• Electronic ISSN: 1939-9359
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 1-6
• DOI: https://doi.org/10.1109/tvt.2024.3439105
• Public URL: https://durham-repository.worktribe.com/output/2762410