Efficient Resource Allocation and Semantic Extraction for Federated Learning Empowered Vehicular Semantic Communication

Semantic communication provides a new paradigm that aims at serving upcoming intelligent transportation applications including autonomous driving and real-time video monitoring. However, the problem of computing efficiency and data privacy during semantic extraction and transmission remains unsolved that need to be further investigated. In this paper, an efficient federated learning-empowered vehicular semantic communication(FVSCom) framework is proposed by jointly considering computing efficiency and data privacy, where federated learning is used to perform semantic extraction. To measure the performance of FVSCom, a metric of semantic utility that jointly considers semantic timeliness and semantic fidelity is proposed. We further analyze the end-to-end delay of the FVSCom network and formulate the semantic utility maximization problem. A DRL-driven dynamic semantic-aware algorithm for semantic utility optimization in FVSCom is proposed. The proposed algorithm can guide the agent to approach the suitable policy of semantic extraction and resource allocation, and dynamically respond to the leave or exit of vehicles. Experimental results showcase the potential of the proposed method for achieving substantial advantages over comparison algorithms and demonstrate strong robustness concerning the departure or exit of vehicles.