Federated – Ensemble Learning (FEL) Techniques on Healthcare Data Privacy: A Review
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2024, Vol 10, Issue 9
Abstract
In the realm of healthcare, protecting patient privacy by harnessing extensive medical data for enhanced clinical outcomes presents a significant challenge. Federated learning (FL) offers a promising solution by enabling collaborative model training without sharing sensitive data. This paper introduces the Privacy-Focused Ensemble Training (PFET) model within the framework of federated ensemble learning (FEL) to bolster data privacy and model performance in hospital environments. The PFET model integrates multiple local models trained independently across different hospitals into a cohesive global model, ensuring patient data remains secure and confined within each institution. Through extensive experiments on diverse medical datasets, our results show that the PFET model in FEL not only achieves high accuracy but also significantly reduces privacy risks compared to traditional centralized approaches. This innovative methodology has the potential to transform privacy-preserving data analysis in healthcare, promoting secure inter-institutional collaboration while safeguarding patient confidentiality.
Authors and Affiliations
Zainab Mukhtar Sani, Ajay Singh Dhabariya, Bachcha Lal Pal, Ahmad Umar Labdo, Jamilu Habu, Babangida S. Imam and Babangida Salisu Muazu
Keywords
Related Articles
- EP ID EP747907
- DOI https://doi.org/10.46501/IJMTST1009024
- Views 29
- Downloads 0
Federated – Ensemble Learning (FEL) Techniques on Healthcare Data Privacy: A Review
In the realm of healthcare, protecting patient privacy by harnessing extensive medical data for enhanced clinical outcomes presents a significant challenge. Federated learning (FL) offers a promising solution by enabling...
Accident Detection & Alert System
This project presents an automotive localization system that utilizes GPS and GSM-SMS services. The system enables the localization of the automobile and transmits its position to the owner's mobile phone via SMS upon re...
AI Driven Technological Drift in Interactive Learning
AI and ML technology are changing how people learn in both education and professional growth. Our approach integrates AI-driven study schedules, resume tracking, student performance analysis, and discussion facilitation...
Prediction of Diabetes in Early Stage through Machine Learning
The goal of this project is to create a system that uses machine learning to forecast the early signs of diabetes. Diabetes is a widespread, long-term condition with serious health consequences, and spotting it early is...
Optimized Academic Schedule Creator for Android Devices
This project provides students, faculty, and administrators a comprehensive platform to manage academic schedules seamlessly. Students and faculty can register and log in, identifying themselves as students or faculty me...