Federated learning network enables secure multicenter clinical research
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
Machine LearningCryptography and SecurityDistributed, Parallel, and Cluster Computing
Summary
Sharing medical data between hospitals is tricky because patient privacy must be protected. The authors found existing systems don't fully meet key needs for secure data collaboration. They built FL-Net, a new tool that helps hospitals safely combine medical data and run studies together without sharing private details. This system also makes it easier to reuse data and research steps for future studies across many hospitals and countries.
What this means in practice
- •For hospital data teams: Coordinate secure, privacy-respecting multicenter clinical studies across hospitals using a single integrated platform for data harmonization and federated analysis.
- •For healthcare it administrators: Manage scalable federated workflows supporting up to 50 clients that ensure reproducible and audited clinical data research without direct data sharing.
Authors
Simon Süwer, Julian Klemm, Elisa Acitelli, Mathieu Almeida, Lucia Altucci, Zsolt Bagyura, Michelangela Barbieri, Zsolt-Zoltán Bedő, Rosaria Benedetti, Béla Bihari, Csongor Csalóka, Lucia Dicunta, Stanislav Ehrlich, Bjoern M. Eskofier, Sándor-József Fejér, Georg Fröwis, Walter Hötzendorfer, Alexandra Kautzky-Willer, Jens Johann Georg Lohmann, Marianna Maranghi, Lorenzo Marconi, Rudolf Mayer, Wouter Leonard Megchelenbrink, Monika Moga, Adham Mottalib, Sanjeev Mehta, Madeleine Müller, Thomas Nyström, Balázs-Attila Orbán, Paul O'Toole, Giuseppe Paolisso, Paolo Parini, Matteo Pedrelli, Enrico Petrillo, Philipp Poindl, Niklas Probul, Anastasia Pustozerova, Tanja Šarčević, Lukas Weilguny, Jan Baumbach, Andreas Maier
Abstract
Federated learning enables collaborative training without sharing patient-level data, but most studies remain simulations. Based on five requirements derived from the literature, we analyzed 14 FL frameworks and found that none fully satisfied these requirements. We present FL-Net, a novel federated clinical research framework to fulfill all requirements. It integrates modular data harmonization, data discovery, disclosure control, securely built versioned FL-Net-Tools and containerized federated workflow execution into a persistent network. It enables the re-use of harmonized data and workflows across studies. FL-Net's end-to-end capabilities were evaluated through harmonization, cross-study patient discovery across MIMIC and US-130, and reproducible, audited federated workflows with up to 50 concurrent clients. FL-Net is being developed within the dAIbetes and Microb-AI-ome EU projects and will cover over 800,000 patients across 10 hospitals in 9 countries covering longitudinal and single point in time data, FL-Net provides a practical foundation for interoperable, reproducible, and privacy-preserving multicenter clinical research.