FedTinyMed: Federated learning enabled tiny multi task machine learning model for smart healthcare monitoring for IoMT

Shakir Khan, Kumar Perumal, Hadeel Alsolai, Abeer Aljohani,
FedTinyMed: Federated learning enabled tiny multi task machine learning model for smart healthcare monitoring for IoMT,
Computers and Electrical Engineering,
Volume 128, Part B,
2025,
110761,
ISSN 0045-7906,
https://doi.org/10.1016/j.compeleceng.2025.110761.
(https://www.sciencedirect.com/science/article/pii/S0045790625007049)
Abstract: This research develops a FedTinyMed, an end-to-end cognitive healthcare model suitable for Internet of Medical Things (IoMT) by combining Tiny Machine Learning (TinyML) and Federated Learning (FL). The model composed of various real time physiological sensors on the wearable devices which includes Inertial Sensors (IS), Pulse Oximeter Sensor (POS), EEG sensor, and Blood Pressure Sensor (BPS) for continuously monitoring the crucial healthcare indicators. A Unified Multi Task Learning Transformer (UMLFormer) is developed by this research which ensures parallel inference among varied healthcare tasks with shared model parameters that effectively diminishes the computational overhead. With the proper training of four benchmark datasets such as Wisconsin, Breast Cancer, PIMA, and Parkinson’s heart disease the proposed research gains end-to-end latency of 100 ms, specificity of 91.5 %, sensitivity of 97.4 %, and F1-score of 95.8 % those superiors the conventional models on the resource constrained devices. By employing Joint Reinforcement Learning enabled Quantization and Pruning (JRL-QP) our research guarantees lesser memory footprint without minimizing the accuracy on real time TinyML deployment. When compared to the state-of-the-art works, the proposed FedTinyMed enables three major advantages which includes (i) higher scalability for decentralized IoMT deployments, (ii) privacy preservation from secure FedAvg aggregation, and cloud independence for ejecting over reliance on higher latency network connections. With those processes the proposed FedTinyMed enables a robust and practical solution for real time smart healthcare monitoring.
Keywords: TinyML; Cloud server (CS); Federated learning (FL); IoMT; and Multi Task Transformer Learning