Federated Learning and 5G/6G‐Based Internet of Medical Things (IoMT): Applications, Key Enabling Technologies, Open Issues and Future Research Directions - UTU Research Portal

A2 Refereed review article in a scientific journal

Federated Learning and 5G/6G‐Based Internet of Medical Things (IoMT): Applications, Key Enabling Technologies, Open Issues and Future Research Directions

Authors: Ahad, Abdul; Ahmed, Kazi Istiaque; Ullah, Farhan; Sheikh, Muhammad Aman; Mohammad, Tahir; Hayajneh, Mohammad; Pires, Ivan Miguel

Publisher: Wiley

Publication year: 2026

Journal: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Article number: e70065

Volume: 16

Issue: 1

ISSN: 1942-4787

eISSN: 1942-4795

DOI: https://doi.org/10.1002/widm.70065

Publication's open availability at the time of reporting: Open Access

Publication channel's open availability : Partially Open Access publication channel

Web address : https://doi.org/10.1002/widm.70065

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/515780177

Self-archived copy's licence: CC BY

Self-archived copy's version: Publisher`s PDF

Abstract

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.

Downloadable publication

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.

WIREs Data Min Knowl - 2026 - Ahad - Federated Learning and 5G 6G‐Based Internet of Medical Things IoMT Applications .pdf

Funding information in the publication:
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.