A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä
Federated Learning and 5G/6G‐Based Internet of Medical Things (IoMT): Applications, Key Enabling Technologies, Open Issues and Future Research Directions
Tekijät: Ahad, Abdul; Ahmed, Kazi Istiaque; Ullah, Farhan; Sheikh, Muhammad Aman; Mohammad, Tahir; Hayajneh, Mohammad; Pires, Ivan Miguel
Kustantaja: Wiley
Julkaisuvuosi: 2026
Lehti: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Artikkelin numero: e70065
Vuosikerta: 16
Numero: 1
ISSN: 1942-4787
eISSN: 1942-4795
DOI: https://doi.org/10.1002/widm.70065
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Osittain avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1002/widm.70065
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/515780177
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
The rapid expansion of smart healthcare technologies has created a growing need for systems that are not only intelligent and efficient, but also deeply respectful of patient privacy. As medical data becomes increasingly distributed across wearables, hospital networks, home-based sensors, and mobile applications, traditional centralized approaches struggle to keep pace with evolving security, latency, and interoperability demands. In this review, we explore federated learning (FL) as a promising pathway towards decentralized intelligence, one that allows healthcare institutions and Internet of Medical Things (IoMT) devices to collaborate without sharing sensitive patient data. Supported by emerging 5G and 6G communication technologies, FL has the potential to reshape modern healthcare by enabling real-time analytics, reliable remote monitoring, personalized treatment recommendations, and advanced medical diagnosis. High-bandwidth, low-latency networks provide the connectivity backbone required for FL to function smoothly across diverse medical environments. We examine FL's various forms, its integration into IoMT applications, and the role of enabling technologies such as edge computing, Device-to-device (D2D) communication, Massive Machine Type Communication (mMTC), Blockchain, Software Defined Networking (SDN), Network Function Virtualization (NFV), Digital twins, and Fog computing. At the same time, we acknowledge that this integration is far from straightforward. Challenges such as data heterogeneity, communication overhead, model drift, security risks, resource allocation, and clinical interoperability continue to shape the research landscape. By synthesizing current findings, identifying open issues, and outlining future research directions, this review provides clarity and drives forward research efforts within the integrated fields of AI, networking, and digital healthcare. This article is categorized under: Application Areas > Health Care.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
This work was supported by the Big Data Analytics Center of United Arab Emirates University under grant code G00004526, funded by national funds through FCT Fundação para a Ciência e a Tecnologia, I.P., and, when eligible, co-funded by EU funds under project/support UID/50008/2025 Instituto de Telecomunicações, and Cardiff Metropolitan University, United Kingdom.
