Federated learning for the healthcare metaverse: Concepts, applications, challenges, and future directions
Abstract
Recent technological advancements have considerably improved healthcare systems to provide various intelligent services, improving life quality. The Metaverse, often described as the next evolution of the Internet, helps the users interact with each other and the environment, thus offering a seamless connection between the virtual and physical worlds. Additionally, the Metaverse, by integrating emerging technologies, such as artificial intelligence (AI), cloud edge computing, Internet of Things (IoT), blockchain, and semantic communications, can potentially transform many vertical domains in general and the healthcare sector (healthcare Metaverse) in particular. The healthcare Metaverse holds huge potential to revolutionize the development of intelligent healthcare systems, thus presenting new opportunities for significant advancements in healthcare delivery, personalized healthcare experiences, medical education, collaborative research, and so on. However, various challenges are associated with the realization of the healthcare Metaverse, such as privacy, interoperability, data management, and security. Federated learning (FL), a new branch of AI, opens up enormous opportunities to deal with the aforementioned challenges in the healthcare Metaverse by exploiting the data and computing resources available at the distributed devices. This motivated us to present a survey on adopting FL for the healthcare Metaverse. Initially, we present the preliminaries of IoT-based healthcare systems, FL in conventional healthcare, and the healthcare Metaverse. Furthermore, the benefits of the FL in the healthcare Metaverse are discussed. Subsequently, we discuss the several applications of FL-enabled healthcare Metaverse, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery. Finally, we highlight the significant challenges and potential solutions toward realizing FL in the healthcare Metaverse.
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Both adversarial disturbance and evasion attacks can disrupt patient results, especially when used in time-sensitive applications like heart monitoring or emergency response.
“Both types of attacks can disrupt patient results, especially when used in time-sensitive applications like heart monitoring or emergency response [69].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [69]
Federated learning allows data to persist within the boundaries of its origin and supplies a decentralized model training method that aligns with healthcare privacy regulations such as GDPR and HIPAA.
“By allowing data to persist within the boundaries of its origin (e.g., hospitals or IoT-enabled smart health devices), FL supplies a decentralized model training method that aligns well with rigid healthcare privacy regulations such as the GDPR and HIPAA [69].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [69]
As the number of participating devices or departments increases, communication and latency can become prohibitive, particularly in settings where real-time processing is critical.
“As the number of participating devices or departments increases, the communication and latency can become prohibitive, particularly in settings where real-time processing is critical [69].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [69]
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The ecological consequences of a pandemic
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Need of 6G for the Metaverse RealizationPreprint
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Self-citationA review of Federated Learning in Intrusion Detection Systems for IoTPreprint
Belenguer, A., Navaridas, J., & Pascual, J. A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.12443 · Cited by 21 · see citing works
Health Monitoring and Diagnosis for Geo-Distributed Edge Ecosystem in Smart City
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Challenges and Solutions for Resilient Telemedicine Services
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The Metaverse for Cognitive Health: A Paradigm Shift
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Self-citationHashash, O., Sharafeddine, S., & Dawy, Z. (2021) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops50388.2021.9473646 · Cited by 13 · see citing works
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(2022) · American Journal of Medical Research · doi:10.22381/ajmr9220222 · Cited by 6 · see citing works
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Flagged first, then by citations
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Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228 · see citing works
Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges
Wang, S., Qureshi, M. A., Miralles-Pechuán, L., Huynh-The, T., Gadekallu, T. R., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3386872 · Cited by 170 · see citing works
Self-citation
- Authors
- Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M.
- Affiliations
- Manchester Metropolitan University (GB)
- Vellore Institute of Technology University (IN)
- Department of Computer and Communications Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam
- Trinity College Dublin (IE)
- Lovely Professional University (IN)
- University College Dublin (IE)
- ORCID
- Venue
- IEEE Internet of Things Journal
- Published
- 2023-12-15
- Volume
- 10
- Issue
- 24
- Pages
- 21873-21891
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4385819771
- Cited by
- 183
- References
- 106
- Citations by year
- 2026: 60 · 2025: 73 · 2024: 36 · 2023: 14
- FWCI
- 17.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- European Union in SPATIAL (101021808)
- CONFIDENTIAL-6G (101096435)
- Science Foundation Ireland under CONNECT Phase 2 Projects (13/RC/2077_P2)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- federated learning, Internet of Medical Things, privacy preservation, interoperability, data management, security (computing), diagnosis, patient monitoring, medical education, infectious diseases, drug discovery, distributed computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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