Federated Learning and Semantic Communication for the Metaverse: Challenges and Potential Solutions

作者：Yue Bian, Xin Zhang, Gadeng Luosang, Duojie Renzeng, Dongzhu Renqing, Xuhui Ding · 发表于：Electronics · 年份：2025 · DOI：10.3390/electronics14050868 · 被引用次数：9 · 研究领域：Privacy-Preserving Technologies in Data、Ethics and Social Impacts of AI、Big Data and Digital Economy

This study investigates the high-quality data processing technology, immersive experience mechanisms, and large-scale access in the Metaverse, concurrently ensuring robust privacy and security. We commence with a comprehensive analysis of the Metaverse’s service requirements, followed by an exploration of its principal technologies. Furthermore, we evaluate the feasibility and potential benefits of integrating semantic communication to enhance the service quality of the Metaverse. A federated semantic communication framework is proposed, integrating semantic data transmission, semantic digital twins, and a Metaverse construction model trained through federated learning. We proceed to assess the performance of our proposed framework through simulations, highlighting the notable enhancements in transmission efficiency, recovery effectiveness, and intelligent recognition ability afforded by semantic communication for the Metaverse. Notably, the framework achieves outstanding compression efficiency with minimal information distortion (0.055), which decreases transmission delays and improves the immersion quality within the Metaverse. Finally, we identify future challenges and propose potential solutions for advancing semantic communication, federated learning, and Metaverse technologies.