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Smart Based IoT Optimization Framework using Federated Learning (FL) Approach for Healthcare
Subject area: Science,Engineering and Technology · Area of research: Machine Learning/Internet of Things
DOI: https://doi.org/10.64388/IREV9I7-1713771
Abstract
The healthcare sector is increasingly leveraging Internet of Things (IoT) technologies to enable real-time patient monitoring, predictive diagnostics, and personalized treatment. However, the large volume of heterogeneous data generated by IoT devices poses challenges in data processing, accuracy, and timely decision-making. This study proposes an IoT optimization system using machine learning (ML) to enhance the efficiency and reliability of healthcare monitoring systems. The framework integrates wearable sensors, smart medical devices, and cloud/edge computing platforms to collect and preprocess physiological and environmental data from patients. These devices generate large volumes of sensitive patient data that require secure, efficient, and privacy-preserving analytics. Conventional cloud-based machine learning approaches often expose raw data to centralized servers, creating significant challenges in terms of data privacy, network congestion, latency, and compliance with healthcare regulations. To address these challenges, this thesis proposes an IoT Device Optimization System using Federated Learning (FL) for intelligent, distributed data processing and device performance enhancement in a healthcare environment. A hybrid Agile?Incremental development methodology was adopted to support iterative prototyping, modular expansion, and continuous validation. Comprehensive testing including unit, integration, system, and performance evaluation demonstrated that the proposed system improves prediction accuracy, minimizes data exposure risk, and enhances device responsiveness. The results show that FL-enabled optimization improves model accuracy (93%), and maintain strict privacy standards. This thesis contributes a scalable, secure, and intelligent framework for optimizing healthcare IoT devices, supporting remote diagnostics, smart clinical environments, and personalized healthcare delivery.
Keywords
Machine Learning, Federated Learning (FL) for Healthcare, IoT based optimization System
How to cite this paper
@article{1713771,
author = {Ajero Chukwuka Evans, Agbakwuru Alphonsus Onyekachi, Ibebuogu Christain Chinwa, Obialor Collins Chimezie},
title = {Smart Based IoT Optimization Framework using Federated Learning (FL) Approach for Healthcare},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {7},
pages = {1763-1776},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713771.pdf},
abstract = {The healthcare sector is increasingly leveraging Internet of Things (IoT) technologies to enable real-time patient monitoring, predictive diagnostics, and personalized treatment. However, the large volume of heterogeneous data generated by IoT devices poses challenges in data processing, accuracy, and timely decision-making. This study proposes an IoT optimization system using machine learning (ML) to enhance the efficiency and reliability of healthcare monitoring systems. The framework integrates wearable sensors, smart medical devices, and cloud/edge computing platforms to collect and preprocess physiological and environmental data from patients. These devices generate large volumes of sensitive patient data that require secure, efficient, and privacy-preserving analytics. Conventional cloud-based machine learning approaches often expose raw data to centralized servers, creating significant challenges in terms of data privacy, network congestion, latency, and compliance with healthcare regulations. To address these challenges, this thesis proposes an IoT Device Optimization System using Federated Learning (FL) for intelligent, distributed data processing and device performance enhancement in a healthcare environment. A hybrid Agile?Incremental development methodology was adopted to support iterative prototyping, modular expansion, and continuous validation. Comprehensive testing including unit, integration, system, and performance evaluation demonstrated that the proposed system improves prediction accuracy, minimizes data exposure risk, and enhances device responsiveness. The results show that FL-enabled optimization improves model accuracy (93%), and maintain strict privacy standards. This thesis contributes a scalable, secure, and intelligent framework for optimizing healthcare IoT devices, supporting remote diagnostics, smart clinical environments, and personalized healthcare delivery.},
keywords = {Machine Learning, Federated Learning (FL) for Healthcare, IoT based optimization System},
month = {January},
doi = {https://doi.org/10.64388/IREV9I7-1713771}
}