Flip-med: federated learning for innovative precision medicine - enhancing data privacy and predictive performance

Privacy-Preserving Chronic Kidney Disease Prediction Through Federated Learning Optimization Techniques

Chronic Kidney Disease (CKD) is a major global health concern that requires accurate and timely prediction for effective diagnosis and clinical decision-making. However, conventional centralized machine learning approaches require sensitive patient data to be collected and shared at a central location, raising signific...

PGCA FL: A PATHWAY CONSENSUS-GUIDED FEDERATED LEARNING FRAMEWORK FOR PRIVACY-PRESERVING DISTRIBUTED GENOMIC CANCER CLASSIFICATION

The growing amount of genomic data has accelerated precision medicine by making it possible to analyze genomic data through artificial intelligence (AI) and classify cancers by their genomes. Yet, there are privacy regulations, institutional ownership of data and restricted access to distributed health resources issues...

Federated Learning in Healthcare Data Analytics: A Privacy-Preserving Approach

The rapid digitization of healthcare has led to an explosion of patient data, necessitating advanced analytics for improved diagnostics, treatment, and predictive modeling. However, traditional centralized data processing poses significant privacy and security risks, particularly concerning sensitive patient informatio...

Federated Learning for Privacy-Preserving Collaborative Data Mining in Healthcare

A privacy preserving federated meta ensemble stacking framework with explainability and differential privacy for chronic kidney disease prediction in resource constrained settings

Findings demonstrate that the proposed framework provides an accurate, privacy-aware, and interpretable solution for decentralized CKD prediction, and maintains robust performance under Gaussian noise.

A Federated Learning Framework with Differential Privacy for High-Accuracy and Privacy-Preserving Healthcare Prediction

This work demonstrates that strong privacy protection and state-of-the-art prediction performance are not mutually exclusive, thereby offering a practical and scalable solution for collaborative healthcare analytics.