Sravan Kumar Elineni
Regulatory constraints and siloed data often hinder collaborative AI in healthcare. Our project supported by ONC/ASTP implements Split Learning to enable three independent HIEs to jointly train a deep learning model without sharing sensitive patient data. We detail the technical workflow (e.g., partial model hosting, secure exchange of activations) and discuss how we navigated real-world challenges in data integration and quality, network security, and regulatory compliance. Preliminary results show robust model performance and seamless interoperability across participating sites, suggesting a robust blueprint for large-scale privacy-preserving ML in healthcare.
Authors: Sean Muir, Dave Carlson, Himali Saitwal, David E Taylor, Chit win, Jayme Welty, Adam Wong, Jordon Everson, Keith Salzman, Serafina Versaggi, Lindsay Cushing, Savannah Mueller
Sravan Kumar Elineni is a seasoned technologist with over a decade of experience in healthcare data systems and emerging fields such as machine learning, robotics, computer vision, and natural language processing. He recently led a high-impact project implementing a state-of-the-art collaborative machine learning system that integrates legal, technological, and security frameworks to facilitate cross-organizational healthcare collaboration.
author = {Sravan Kumar Elineni},
title = {Breaking Barriers, Not Privacy: {Real-World} Split Learning across Healthcare Systems},
year = {2025},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = jun
}
