Blockchain-empowered Federated Learning for Healthcare Metaverses:
  User-centric Incentive Mechanism with Optimal Data Freshness
Jiawen Kang Jinbo Wen Dongdong Ye Bingkun Lai Tianhao Wu Zehui Xiong Jiangtian Nie Dusit Niyato Yang Zhang Shengli Xie
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.