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。

区块链驱动的车联网联邦学习方法

Blockchain-Enabled Federated Learning Approach for Vehicular Networks

End-to-end training with global optimization have popularized graph neural
networks (GNNs) for node classification, yet inadvertently introduced
vulnerabilities to adversarial edge-perturbing attacks. Adversaries can exploit
the inherent opened interfaces of GNNs' input and output, perturbing critical
edges and thus manipulating the classification results. Current defenses, due
to their persistent utilization of global-optimization-based end-to-end
training schemes, inherently encapsulate the vulnerabilities of GNNs. This is
specifically evidenced in their inability to defend against targeted secondary
attacks. In this paper, we propose the Graph Agent Network (GAgN) to address
the aforementioned vulnerabilities of GNNs. GAgN is a graph-structured agent
network in which each node is designed as an 1-hop-view agent. Through the
decentralized interactions between agents, they can learn to infer global
perceptions to perform tasks including inferring embeddings, degrees and
neighbor relationships for given nodes. This empowers nodes to filtering
adversarial edges while carrying out classification tasks. Furthermore, agents'
limited view prevents malicious messages from propagating globally in GAgN,
thereby resisting global-optimization-based secondary attacks. We prove that
single-hidden-layer multilayer perceptrons (MLPs) are theoretically sufficient
to achieve these functionalities. Experimental results show that GAgN
effectively implements all its intended capabilities and, compared to
state-of-the-art defenses, achieves optimal classification accuracy on the
perturbed datasets.

本文提出了 Graph Agent Network，一个图结构的代理网络，用于解决节点分类的抵抗者攻击。GAgN 通过节点之间的去中心化交互，可以学习推断节点的全局感知来执行任务，从而防止恶意消息在整个系统中传播，在扰动的数据集上实现了最佳的分类准确性。