Federated Learning for Computational Offloading and Resource Management of Vehicular Edge Computing in 6G-V2X Network

The Sixth Generation network (6G) can support autonomous driving along with various vehicular applications like Vehicular Edge Computing (VEC), a distributed computing architecture for connected autonomous vehicles. Computational offloading and resource management of Vehicular Edge Computing can help sort out some issues, such as high communication costs, privacy protection, an excessively long training process, etc., by proposing an efficient training model of the Federated Learning for computational offloading and resource management in a vehicular environment. Two research issues are highlighted in this paper. One problem is related to the current offloading system: the smart structure and operating system. Consistent access to cloud computing services, regardless of the installed operating system or used hardware, is still challenging. Another issue is related to security and privacy. Security and privacy are two important features that should be maintained in cloud data centers and data transmission during offloading and resource management. In this survey paper, a system is going to be proposed which will give a partial solution for these issues. The proposed solution, which is found while conducting this review, offers a system that can train a model and help update the edge devices' information. The entire edge cloud system can provide updated information for edge devices and can solve the difficulties of getting some key information necessary for model-related optimization. This also can enhance the effectiveness of the frameworks of the 6G-V2X network for communication.