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
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- EP ID EP747907
- DOI https://doi.org/10.46501/IJMTST1009024
- Views 76
- Downloads 0
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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...