A Distributed Learning Architecture for Semantic Communication in Autonomous Driving Networks for Task Offloading

Semantic communication based on machine learning (ML) techniques emerged as a new transmission paradigm that can significantly improve spectrum efficiency. It looks promising for improving the task of offloading quality of service (QoS) for autonomous driving networks (ADNs) where autonomous vehicles require a significant amount of communication with the vehicle edge clouds (VECs). However, in practical ADNs, updating the ML-based semantic communication coder model is affected by various unique factors such as mobility and privacy considerations. Therefore, in ADNs, the conventional ML frameworks are not directly applicable to updating semantic communication coders. In this article, we discuss the unique challenges faced by updating the semantic communication coder in ADNs, and review the existing ML frameworks. To address these challenges, we further propose a privacy-preserving personalized federated learning (3PFL) framework for updating the semantic communication coder in ADNs. Simulation results confirm the effectiveness of 3PFL for this process.