A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx — 2025-08-15
Summary
The article presents a robust pipeline for applying Federated Learning (FL) with Differential Privacy (DP) to imbalanced clinical data, specifically targeting cardiovascular disease prediction. By integrating SMOTETomek for data balancing and FedProx for handling non-IID data, the study achieves a significant improvement in model recall while maintaining strong privacy guarantees, emphasizing a practical approach to balancing the privacy-utility trade-off in healthcare applications.
Why This Matters
This research is crucial as it addresses the challenges of using sensitive and imbalanced medical data while preserving patient privacy. By demonstrating a methodology that leverages advanced machine learning techniques, the study provides healthcare institutions with a framework to collaboratively develop more accurate and privacy-preserving predictive models, which are essential for early disease detection and better patient outcomes.
How You Can Use This Info
Professionals in healthcare and data science can apply these findings to enhance their predictive analytics capabilities while ensuring compliance with privacy regulations. By using the outlined pipeline, organizations can leverage FL and DP to collaborate on building robust diagnostic models without compromising patient data privacy, thus facilitating more effective and secure health research collaborations.