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arXiv 2304.00524cs.CYcs.AI
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面向医疗保健元宇宙的联邦学习综述：概念、应用、挑战与未来方向

A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions

Manchester Metropolitan University（曼彻斯特城市大学）；Vellore Institute of Technology（韦洛尔理工学院）；Trinity College Dublin, The University of Dublin（都柏林大学圣三一学院）；Lebanese American University（黎巴嫩美国大学）；Ho Chi Minh City University of Technology and Education（胡志明市技术教育大学）；University College Dublin（都柏林大学学院）
Ali Kashif Bashir, Nancy Victor, Sweta Bhattacharya, Thien Huynh-The, Rajeswari Chengoden, Gokul Yenduri, Praveen Kumar Reddy Maddikunta, Quoc-Viet Pham, Thippa… 展开作者

Ali Kashif Bashir, Nancy Victor, Sweta Bhattacharya, Thien Huynh-The, Rajeswari Chengoden, Gokul Yenduri, Praveen Kumar Reddy Maddikunta, Quoc-Viet Pham, Thippa Reddy Gadekallu, Madhusanka Liyanage

备注：
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AI总结：

本文综述了联邦学习（FL）在医疗保健元宇宙中的应用，探讨了其在隐私保护、数据管理及低延迟服务等方面的优势，并分析了医疗诊断、患者监测等应用场景及未来挑战。

摘要：

近期的技术进步显著改善了医疗保健系统，使其能够提供各种智能医疗保健服务并提高生活质量。联邦学习（FL）作为人工智能（AI）的新分支，为处理医疗保健系统中的隐私问题以及利用分布式设备上可用的数据和计算资源带来了机遇。此外，Metaverse通过整合AI、云边缘计算、物联网（IoT）、区块链和语义通信等新兴技术，改变了许多垂直领域，尤其是医疗保健领域。显然，FL展现出诸多益处，并为传统和Metaverse医疗保健提供了新机遇，这促使我们针对FL在Metaverse医疗保健系统中的应用进行综述。首先，我们介绍了基于IoT的医疗保健系统、传统医疗保健中的FL以及Metaverse医疗保健的预备知识。然后讨论了FL在Metaverse医疗保健中的益处，包括改善隐私和可扩展性、更好的互操作性、更好的数据管理、额外的安全性，以及自动化和低延迟的医疗保健服务。随后，我们讨论了与启用FL的Metaverse医疗保健相关的若干应用，包括医疗诊断、患者监测、医学教育、传染病和药物发现。最后，我们强调了在Metaverse医疗保健中实现FL所面临的重大挑战和潜在解决方案。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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