Human digital twin (HDT) is an innovative concept that creates digital representations of humans to support human-centric services by enabling real-time data synchronization between the physical twin (PT) and the virtual twin (VT). This PT-VT synchronization process is inherently data-intensive, requiring efficient resource management, especially in resource-constrained environments. Semantic communication has emerged as a promising alternative to traditional data-driven methods. However, single-encoder models may struggle to meet the dynamic requirements of HDT applications, particularly when resources are limited. To address these challenges, this paper introduces a multi-encoder semantic communication model that dynamically allocates resources—such as bandwidth and computational frequency—on specific application demands. The framework is formulated as a mixed-integer nonlinear programming (MINLP) problem and is solved using a genetic algorithm (GA) based approach. Simulation results demonstrate that the proposed model optimizes synchronization while effectively managing power consumption, outperforming traditional single-encoder models in terms of accuracy, latency, and resource efficiency. This dynamic multi-encoder approach offers a scalable and adaptable solution for future HDT applications.

Artificial intelligence-generated content (AIGC) has been proposed as a solution to meet the requirements of ultra-reliable, secure, and privacy-preserving connectivity in human digital twin (HDT) networks. In such an AIGC-enhanced HDT, contents representing the true statuses of physical twins are generated in the virtual environment for the immediate update and evolution of the corresponding virtual twins (VTs). However, adopting a distributed AIGC in HDT presents several challenges, including the need for personalized VTs, data privacy concerns, and insufficient contextual understanding. This paper introduces a multi-layer federated semantic learning framework to address these challenges, incorporating batch learning to meet the training requirements for semantic-channel encoders and decoders. Furthermore, we introduce a novel user association framework to maximize the overall system performance under shard formation constraints. We then formulate a long-term joint optimization problem for user selection over finite learning periods. A novel Lyapunov-based online optimization strategy was proposed to mitigate the impact of time-varying and unpredictable training conditions. Additionally, we introduce a multi-arm bandit-based method and a context-centric user selection approach to solve the optimization problem. The results demonstrate that the proposed user association framework addresses the limitations of existing approaches, thereby improving the overall performance of the multi-shard AIGC-enhanced HDT.