Federated Learning and Edge Caching for Enhancing Security and Efficiency in IoV Networks

Abstract

In the evolving Internet of Vehicles (IoV) landscape, ensuring security and efficiency is critical. This study integrates Federated Learning (FL) with edge caching to address these challenges. FL allows decentralized model training, safeguarding data privacy and reducing communication overhead. Coupled with edge caching, latency and bandwidth consumption are minimized by storing frequently accessed data at the network edge. Our approach optimizes resource use with parameter tuning, momentum for faster convergence, and parallel processing for scalability. Results demonstrate that this combination significantly enhances IoV network performance, paving the way for secure, efficient vehicular communication systems