Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services. The healthcare metaverses allow for effective decision-making and data analytics for users. However, there still exist critical challenges in building healthcare metaverses, such as the risk of sensitive data leakage and issues with sensing data security and freshness, as well as concerns around incentivizing data sharing. In this paper, we first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. To further improve the privacy protection of healthcare metaverses, a cross-chain empowered FL framework is utilized to enhance sensing data security. This framework utilizes a hierarchical cross-chain architecture with a main chain and multiple subchains to perform decentralized, privacy-preserving, and secure data training in both virtual and physical spaces. Moreover, we utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing in a user-centric manner. This model exploits PT to better capture the subjective utility of the service provider. Finally, our numerical results demonstrate the effectiveness of the proposed schemes for healthcare metaverses.

翻译：鉴于元宇宙的革命性作用，医疗元宇宙正成为一种变革力量，通过创建智能医疗系统提供沉浸式个性化服务。医疗元宇宙使用户能够进行高效决策和数据分析，然而构建医疗元宇宙仍面临关键挑战，包括敏感数据泄露风险、感知数据安全性与新鲜度问题，以及数据共享激励机制的不足。本文首先设计了一种基于去中心化联邦学习的用户中心隐私保护框架，用于医疗元宇宙。为增强医疗元宇宙的隐私保护，进一步采用跨链赋能的联邦学习框架提升感知数据安全性，该框架通过主链与多子链的分层跨链架构，在虚拟空间和物理空间中实现去中心化、隐私保护且安全的数据训练。此外，我们采用信息年龄作为数据新鲜度有效度量，并提出基于前景理论的AoI契约理论模型，以用户为中心激励感知数据共享。该模型利用前景理论更准确地捕捉服务提供者的主观效用。最后，数值结果验证了所提方案在医疗元宇宙中的有效性。