Title: A decentralised asynchronous federated learning framework for autonomous driving

Authors: Xiaoli Li; Ting Cai; Wei Xiong; Degang Xu

Addresses: Computer School, HuBei University of Arts and Science, Xiangyang, China ' School of Computer Science and Engineering, Hubei University of Technology, Wuhan, China ' Computer School, HuBei University of Arts and Science, Xiangyang, China ' Computer School, HuBei University of Arts and Science, Xiangyang, China

Abstract: Traditional autonomous driving usually requires a large number of vehicles to upload data to a central server for training. However, collecting data from vehicles may violate personal privacy as road environmental information contains geographic location. Federated learning can achieve multi-vehicle collaborative sensing of the road environment while protecting data privacy. However, the existing centralised federated learning architecture faces some challenges, such as credibility, fairness, and real-time. To address the above issues, we propose a decentralised asynchronous federated learning framework based on blockchain. Firstly, using blockchain to replace the central server of traditional federated learning architecture avoids the untrustworthy issues caused by the central architecture. Secondly, the blockchain module includes scoring contract units and incentive contract units to prevent malicious vehicle attacks and designs fair incentive mechanisms to ensure the ecological health and sustainable development of federated learning. Thirdly, using the asynchronous federated learning algorithm, blockchain can immediately aggregate model updates from vehicles, greatly improving the overall training flexibility and real-time performance. Experimental results demonstrate the effectiveness of the proposed framework.

Keywords: autonomous driving; blockchain; federated learning; asynchronous; decentralised.

DOI: 10.1504/IJVAS.2023.140518

International Journal of Vehicle Autonomous Systems, 2023 Vol.17 No.3/4, pp.133 - 149

Received: 01 Oct 2023
Accepted: 07 Mar 2024
Published online: 21 Aug 2024 *

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