【Author】
Chai, Haoye; Leng, Supeng; Chen, Yijin; Zhang, Ke
【Source】IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
【Abstract】Internet of Vehicles (IoVs) is highly characterized by collaborative environment data sensing, computing and processing. Emerging big data and Artificial Intelligence (AI) technologies show significant advantages and efficiency for knowledge sharing among intelligent vehicles. However, it is challenging to guarantee the security and privacy of knowledge during the sharing process. Moreover, conventional AI-based algorithms cannot work properly in distributed vehicular networks. In this paper, a hierarchical blockchain framework and a hierarchical federated learning algorithm are proposed for knowledge sharing, by which vehicles learn environmental data through machine learning methods and share the learning knowledge with each others. The proposed hierarchical blockchain framework is feasible for the large scale vehicular networks. The hierarchical federated learning algorithm is designed to meet the distributed pattern and privacy requirement of IoVs. Knowledge sharing is then modeled as a trading market process to stimulate sharing behaviours, and the trading process is formulated as a multi-leader and multi-player game. Simulation results show that the proposed hierarchical algorithm can improve the sharing efficiency and learning quality. Furthermore, the blockchain-enabled framework is able to deal with certain malicious attacks effectively.
【Keywords】Hierarchical blockchain; federated learning; knowledge sharing
【标题】一种基于分层区块链的联邦学习算法，用于车联网中的知识共享
【摘要】车联网 (IoV) 的特点是协同环境数据传感、计算和处理。新兴的大数据和人工智能 (AI) 技术在智能汽车之间的知识共享方面显示出显着的优势和效率。然而，在共享过程中保证知识的安全性和隐私性具有挑战性。此外，传统的基于人工智能的算法无法在分布式车辆网络中正常工作。本文提出了一种分层区块链框架和分层联邦学习算法进行知识共享，车辆通过机器学习方法学习环境数据并相互共享学习知识。所提出的分层区块链框架对于大规模车辆网络是可行的。分层联邦学习算法旨在满足 IoV 的分布式模式和隐私要求。然后将知识共享建模为交易市场过程以刺激共享行为，并将交易过程制定为多领导和多人游戏。仿真结果表明，提出的分层算法可以提高共享效率和学习质量。此外，支持区块链的框架能够有效地应对某些恶意攻击。
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