Optimizing Content Cache With Vehicular Edge Computing: A Deep Federated Learning-Based Novel Predictive Study

This paper proposes an innovative framework for enhancing vehicular edge computing (VEC) efficiency by addressing challenges in traditional intelligent transportation systems and Internet of vehicles. Our aim is to improve content prediction accuracy, reduce offload latency, and ensure data privacy by utilizing advanced techniques such as federated learning (FL) and deep reinforcement learning. We propose an in-car popularity-based caching (PBC) asynchronous system, based on FL, for edge computing. Unlike traditional synchronous FL, asynchronous FL allows the global model to be updated without waiting for all cars to complete learning process and upload their individual models, which helps to reduce training time. Integrating VEC with roadside infrastructure system allows real-time data processing by enabling low latency and high-quality services for applications like traffic management and autonomous driving. Through collaborative caching schemes and personalized content delivery, the proposed model optimizes system performance while adapting to dynamic vehicular environments. By prefetching potentially popular content ahead of time and caching it in edge nodes or roadside units, depending on the position and movement direction of the vehicle, the PBC system reduces the delay in content queries. Comprehensive experimental evaluations demonstrate that the PBC technique outperforms existing benchmark caching schemes in reducing the latency.