GenFV: AIGC-Assisted Federated Learning for Vehicular Edge Intelligence

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Abstract
Federated Learning (FL) has emerged as a promising technology for privacy-preserving vehicular applications. However, its performance is frequently limited by challenges such as vehicle mobility, unstable wireless channels, and heterogeneous data distributions. To address these challenges, we propose GenFV, a novel Artificial Intelligence-Generated Content (AIGC)-assisted Federated Learning for Vehicular Edge Intelligence (VEI) framework. By leveraging AIGC for data synthesis, GenFV enhances the performance of FL models in dynamic vehicular environments. We introduce a weighted policy based on Earth Mover's Distance (EMD) to quantify data heterogeneity, and formulate a system time minimization problem, which is a mixed-integer non-linear programming (MINLP) problem. To solve this, we first tackle vehicle selection, and then transform and decompose the problem to optimize resource allocation for bandwidth, transmission power, and generated data. Experimental results show that GenFV significantly outperforms existing approaches, improving both the performance and robustness of FL in resource-constrained vehicular networks.

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