International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A Secure AI-Enabled IoT Framework for Smart Diabetes Monitoring Using Edge-Cloud and Federated Learning

Author(s) Ms. Meena Sharma, Dr. Abhilasha Dangi
Country India
Abstract A chronic disease, diabetes is very prevalent among the people of the world with millions of people having the disease and thereby increasing the burden on healthcare services. to the health care systems. According to the world Health Organization (WHO) the prevalence of diabetes continues to rise particularly in the developing countries [1]. Traditional techniques of glucose monitoring are non-real time, invasive, episodic, and do not include real time analytics. The combination of the Internet of Things (IoT), Artificial Intelligence (AI), wearable sensors, edge computing, block chain, and federated learning proposes a promising solution in the form of continuous and secure management of diabetes [2].The present paper suggests a safe AI-enabled IoT-based smart diabetes monitoring system that combines wearable continuous glucose monitoring (CGM) sensor, edge analytics, cloud storage, federated learning, and block chain-based security. The proposed glucose prediction and anomaly detection management system is based on deep learning models to forecast glucose and detect abnormalities without infringing patient privacy because of the decentralized learning processes [3], [4]. The block chain technology ensures data integrity, transparency, and access control in healthcare data sharing spaces is limited [5].The experiment is validated by using the OhioT1DM Dataset [6]. Results show that edge and privacy-aware distributed learning solutions can be helpful in improving the accuracy and latency of glucose prediction, even when compared to the existing cloud-based systems.
Keywords IoT, Diabetes Monitoring, Edge Computing, Federated Learning, Block chain, Deep Learning, CGM, Privacy Preservation
Field Computer Applications
Published In Volume 8, Issue 5, September-October 2026
Published On 2026-09-29
DOI https://doi.org/10.36948/ijfmr.2026.v08i05.88678
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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