Graph-Enhanced Explainable Asynchronous Federated Learning for Internet of Vehicles

Prevailing interest and advancement of fifth-generation (5G)-related technology has inspired the study of the Internet of Vehicles (IoV). As per the request for privacy-preserving and collaborative learning, the federated learning (FL) paradigm has emerged as a significant supporting component, but it also requires facilitating the explainability of the incentive mechanism. Inspired by this, an asynchronous FL paradigm with graph-based reputation incentives has been proposed in this article for trajectory prediction tasks in IoV cases. According to prominent characteristics of IoV scenarios, where inter-relations of clients impose great effects on trajectory prediction, a feature extraction strategy based on graph neural network (GNN) is proposed to extract latent features of IoV data and establish a reputation-based incentive mechanism. Integrated with this incentive scheme, an asynchronous FL framework is proposed to carry out collaborative trajectory forecasting tasks. As illustrated by the testing results on real-world datasets, the proposed scheme is capable of promoting overall FL performance greatly. Additionally, more experiments on proposed reputation incentives have been conducted and show an advance over loss function-based reputation incentives.