Cybertwin-Driven Federated Learning Based Personalized Service Provision for 6G-V2X

The rapid growth of Autonomous Vehicle (AV) technology and the integration of edge computing grasp new challenges along with the ever-increasing mobile internet traffic and services. Tackling such challenges through customized edge computing services is the critical research in 6G Vehicle-to-Everything (6G-V2X) communication. V2X contributes detailed information about the current navigation of vehicles, automatic payments for toll roads, parking fees and other services. With the countless, unique, and personalized service requirements of AVs over computation-intensive applications, exploring the edge resources for the excellent Quality of Service (QoS) provision is the greatest concern. This paper proposes a Federated Learning and edge Cache-assisted Cybertwin (FLCC) framework for personalized service provision in 6G-V2X. Integration of cybertwin in 6G enables the connectivity of the physical system to the digital realm, allowing for adequate instantaneous wireless access. The FLCC jointly considers the edge cooperation and optimizations through the proposed Federated Multi-agent Deep Reinforcement Learning based (FM-DRL) algorithm. The FM-DRL algorithm balances the FLCC’s learning accuracy. It minimizes the time and cost by taking the factors such as cybertwin association, training data batch size, and bandwidth. Finally, caching is performed using the Federated Reinforcement Learning-based Edge Caching (FREC) algorithm to obtain the desired datasets required that train the model for providing personalized 6G-V2X services for the AVs. Numerical studies and simulation results reveal that the proposed system outperforms the baseline learning approaches by 17.6%.