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

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