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

• URL: http://arxiv.org/abs/2304.00524v2
• Date: Wed, 5 Apr 2023 01:57:11 GMT
• Title: A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions
• Authors: Ali Kashif Bashir, Nancy Victor, Sweta Bhattacharya, Thien Huynh-The, Rajeswari Chengoden, Gokul Yenduri, Praveen Kumar Reddy Maddikunta, Quoc-Viet Pham, Thippa Reddy Gadekallu and Madhusanka Liyanage
• Abstract summary: Federated learning (FL), a new branch of artificial intelligence (AI), opens opportunities to deal with privacy issues in healthcare systems. We present preliminaries to IoT-based healthcare systems, FL in conventional healthcare, and Metaverse healthcare. We discuss several applications pertaining to FL-enabled Metaverse healthcare, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery.
• Score: 14.1427616718447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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