Optimized Multi-Scale Semantic Parameter Selection and Transmission for Vehicular Edge Computing Networks

With the advancement of intelligent driving technology, vehicular networks generate vast amounts of decentralized data that need to be processed. As a distributed paradigm, Federated Learning (FL) enables data integration and processing across various vehicles. However, traditional FL methods face significant challenges in vehicular networks, including high communication overhead and the difficulty of meeting strict latency and reliability requirements. To address these challenges, we propose a Multi-scale Semantic Selection-based FL (MSSFL) scheme, which integrates multi-scale semantic parameter selection and transmission optimization to reduce the system's communication cost. The proposed scheme selects parameters with high semantic importance and allocates bandwidth proportionally based on their quantity to enhance communication efficiency. We further formulate an optimization problem to minimize both parameters' transmission cost and upload delay. To solve this problem, we develop an alternating iterative solution using the block coordinate descent (BCD) method, which alternately optimizes the semantic parameter selection and bandwidth allocation strategy. Experimental results validate the effectiveness of the proposed framework in enhancing both communication efficiency and model accuracy.