Asynchronous federated learning with GNN for enhancing data security in internet of vehicles

To addresses the security and efficiency problem in the Internet of Vehicles (IoV) ecosystem, where vast data exchanges between vehicles and roadside units via smart devices pose potential security, privacy, and data integrity risks. To combat these issues and improve user experience without compromising data quality, a cutting-edge framework that integrates Graph Neural Network with federated learning is proposed. This solution not only secures vehicular data privacy and integrity but also reduces unnecessary data transmissions by enabling local data storage, thus easing the load on communication networks. Additionally, I introduce a consensus mechanism powered by edge computing to minimize latency and enhance data exchange reliability, leveraging distributed computing to mitigate network congestion and scale the IoV infrastructure efficiently. Central to our framework is a reputation system that assesses user contributions, incentivizing honesty and reliability while deterring malicious activities. Our approach promises a more secure, efficient, and scalable IoV environment, showcasing the pivotal role of advanced technologies in addressing current data exchange challenges.