Federated Learning for V2X Communications: Opportunities and Challenges

This paper delves into the potential of leveraging federated learning (FL) techniques to revolutionize vehicle-to-everything (V2X) wireless communications. We examine the significant opportunities that FL offers in the V2X domain, including distributed model training, privacy preservation and enhanced communication efficiency. Concurrently, we spotlight the formidable challenges associated with adapting FL to the unique characteristics of highly dynamic and heterogeneous vehicular networks. These challenges encompass Non - Independent Identically Distribution (Non-IID) data distribution, ensuring model reliability and addressing scoring disparities among participating vehicles. To navigate this intricate landscape, we propose novel research directions, including hierarchical FL, incentive mechanisms and the development of robust training algorithms, aimed at surmounting these obstacles. This paper draws upon valuable insights gleaned from an initial analysis, along with simulation results that underscore the efficacy of tailored FL designs in unlocking the transformative potential of collective learning within vehicular networks, while simultaneously mitigating systemic limitations.