Authors : Hemalatha A.

Volume/Issue : Volume 10 - 2025, Issue 10 - October

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DOI : https://doi.org/10.38124/ijisrt/25oct1178

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Abstract : Internet of Medical Things (IoMT) is revolutionizing the healthcare sector around the world by installing large networks of interconnected sensors and devices. However, the sheer volume, real-time nature, and sensitivity of health data put a strain on traditional centralized cloud designs, raising critical issues around latency and energy usage, as well as concerns related to privacy regulations such as HIPAA and GDPR. This review assesses the necessary architectural changes towards decentralized intelligence, with a focus on the synergy between Edge Artificial Intelligence (Edge-AI) and Federated Learning (FL). The Edge-AI minimizes latency by enabling the processing of real-time data on-device, which is crucial for life-sensitive applications. FL provides a privacy-sensitive protocol that addresses the issue of the data silo in healthcare and allows global models to be trained jointly by distributed medical institutions without sharing sensitive raw data. We thoroughly consider the architectural paradigms, the specific applications of IoMT, and the taxonomy of security risks, including physical tampering and data poisoning. We also outline the key privacy-enhancement methods (PETs), including Differential Privacy and Homomorphic Encryption, the trade-offs (e.g., accuracy versus efficiency) inherent in them, and the main research gaps, and conclude with the key future directions, including 6G connectivity.

Keywords : Edge Computing; Federated Learning; Edge-AI;Deep Learning;Healthcare.

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