Recent technological advancements have considerately improved healthcare
systems to provide various intelligent healthcare services and improve the
quality of life. Federated learning (FL), a new branch of artificial
intelligence (AI), opens opportunities to deal with privacy issues in
healthcare systems and exploit data and computing resources available at
distributed devices. Additionally, the Metaverse, through integrating emerging
technologies, such as AI, cloud edge computing, Internet of Things (IoT),
blockchain, and semantic communications, has transformed many vertical domains
in general and the healthcare sector in particular. Obviously, FL shows many
benefits and provides new opportunities for conventional and Metaverse
healthcare, motivating us to provide a survey on the usage of FL for Metaverse
healthcare systems. First, we present preliminaries to IoT-based healthcare
systems, FL in conventional healthcare, and Metaverse healthcare. The benefits
of FL in Metaverse healthcare are then discussed, from improved privacy and
scalability, better interoperability, better data management, and extra
security to automation and low-latency healthcare services. Subsequently, we
discuss several applications pertaining to FL-enabled Metaverse healthcare,
including medical diagnosis, patient monitoring, medical education, infectious
disease, and drug discovery. Finally, we highlight significant challenges and
potential solutions toward the realization of FL in Metaverse healthcare.

本文介绍了联邦学习在元宇宙医疗保健领域中的应用，包括医疗诊断、患者监测、医学教育、传染病和药物发现等方面，强调了联邦学习在提升隐私性、可扩展性、互操作性、数据管理、安全性及低延迟医疗服务等方面的重要优点和挑战。