Data from interconnected vehicles may contain sensitive information such as
location, driving behavior, personal identifiers, etc. Without adequate
safeguards, sharing this data jeopardizes data privacy and system security. The
current centralized data-sharing paradigm in these systems raises particular
concerns about data privacy. Recognizing these challenges, the shift towards
decentralized interactions in technology, as echoed by the principles of
Industry 5.0, becomes paramount. This work is closely aligned with these
principles, emphasizing decentralized, human-centric, and secure technological
interactions in an interconnected vehicular ecosystem. To embody this, we
propose a practical approach that merges two emerging technologies: Federated
Learning (FL) and Blockchain. The integration of these technologies enables the
creation of a decentralized vehicular network. In this setting, vehicles can
learn from each other without compromising privacy while also ensuring data
integrity and accountability. Initial experiments show that compared to
conventional decentralized federated learning techniques, our proposed approach
significantly enhances the performance and security of vehicular networks. The
system's accuracy stands at 91.92\%. While this may appear to be low in
comparison to state-of-the-art federated learning models, our work is
noteworthy because, unlike others, it was achieved in a malicious vehicle
setting. Despite the challenging environment, our method maintains high
accuracy, making it a competent solution for preserving data privacy in
vehicular networks.

这项研究提出了一种结合联邦学习和区块链的实用方法，用于构建一个分散化的车辆网络，以解决互联车辆系统中的数据隐私和安全问题，并展示了相比常规的分散化联邦学习技术，该方法在车辆网络的性能和安全性方面显著提升。