Mobile artificial intelligence-generated content (AIGC) is an innovative technology that can support the evolution and updating processes of virtual twins (VTs) in human digital twin (HDT) systems. With a reliable and efficient automatic information 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 rare disease data and providing timely customized services, it may suffer from poor understanding of contexts, lack of creativity, and various security and privacy concerns. In this paper, we propose a new framework, which integrates federated learning (FL) and semantic communication (SemCom) to enhance performance in such a system while improving accuracy and convergence properties. Such an integrated FL-enhanced SemCom (FLeS) solution, however, comes with its own challenges. First, we present a holistic architectural framework for the proposed FLeS paradigm for mobile AIGC-enabled HDT systems and discuss the associated design requirements and challenges. We later present some key technologies necessary to realize such a solution before elaborating on some technical issues to suggest future directions. We believe that this article will open up new research opportunities and motivate new research efforts toward incorporating FLeS techniques for mobile AIGC, especially in emerging mobile services such as HDT.

This paper proposes a new secure and privacy-preserving prediction-enhanced solution for reliable physical-to-virtual communications in human digital twin (HDT) systems. With such a prediction-enhanced connectivity (PeHDT) framework, the evolution of any virtual twin (VT) could be triggered in real-time or in advance using the expected state of its physical counterpart. This ensures the continuous maintenance of a true replica of each physical twin (PT), thus relieving the need for timely PT-VT synchronization while the VT-experienced delay is reduced to zero or close to zero. We adopted a secured federated multi-task learning technique to meet the security and privacy constraints of HDT and employed a single server discrete-time batch-service queue framework when characterizing the batching process to reduce the communication burden. Furthermore, we introduced a prediction verification framework to improve the performance of the proposed PeHDT framework. The resulting problem was formulated as a constrained Markov decision process and was solved by introducing a primary-dual deep deterministic policy gradient (DDPG) algorithm. Through a joint investigation of communication, batching and prediction verification schemes, the simulation results show that the proposed PeHDT framework can greatly reduce both the VT-experienced delay and the PT-VT communication time without compromising the specific requirements of HDT.

This paper presents a new, secure, privacy-preserving and efficient human-to-virtual twin connectivity scheme for HDT by integrating three key techniques: differential privacy, federated multi-task learning and blockchain.

This paper presents a blockchain-enabled data sharing framework for latency-sensitive computing services.