FLeS: A Federated Learning-Enhanced Semantic Communication Framework for Mobile AIGC-Driven Human Digital Twins
Samuel D. Okegbile, Haoran Gao, Oluwasegun Talabi, Jun Cai, Changyan Yi, Dusit Niyato, Xuemin Shen
Abstract
Innovative mobile artificial intelligence-generated content (AIGC) can support the evolution and updating processes of virtual twins (VTs) in human digital twin (HDT) systems. With a reliable and efficient automatic data generation process, the requirement for a timely physical-to-virtual synchronization in HDT can be satisfied. While such an AIGC-enabled HDT system can facilitate modelling high fidelity VTs, generating content that represents the true states in the physical environment and providing timely customized services, it may suffer from a poor understanding of contexts, a lack of creativity, and various security and privacy concerns. In this paper, we propose a novel framework, which integrates federated learning (FL) and semantic communication (SemCom) to enhance performance in the AIGC-enabled HDT system while improving accuracy and convergence properties. First, we present a holistic architectural framework for the proposed FL-enhanced SemCom (FLeS) solution for mobile AIGC-enabled HDT systems and discuss its design requirements and challenges. We later present key technologies necessary to realize the FLeS solution, followed by elaborating on important technical issues to suggest future directions. Experimental results demonstrate that FLeS not only facilitates reliable and personalized content generation but also shows better performance when compared to existing solutions.