FL-Net is a federated learning framework designed for medical research that addresses five key requirements identified in the literature. It offers modular data harmonization, discovery, disclosure control, versioned tools, and containerized workflow execution, enabling reuse of harmonized data and workflows across studies. The framework was evaluated on MIMIC and US-130 datasets, supporting up to 50 concurrent clients and demonstrating reproducible, audited federated workflows.
By Simon S\"uwer, Julian Klemm, Elisa Acitelli, Mathieu Almeida, Lucia Altucci, Zsolt Bagyura, Michelangela Barbieri, Zsolt-Zolt\'an Bed\H{o}, Rosaria Benedetti, B\'ela Bihari, Csongor Csal\'oka, Lucia Dicunta, Stanislav Ehrlich, Bjoern M. Eskofier, S\'andor-J\'ozsef Fej\'er, Georg Fr\"owis, Walter H\"otzendorfer, Alexandra Kautzky-Willer, Jens Johann Georg Lohmann, Marianna Maranghi, Lorenzo Marconi, Rudolf Mayer, Wouter Leonard Megchelenbrink, Monika Moga, Adham Mottalib, Sanjeev Mehta, Madeleine M\"uller, Thomas Nystr\"om, Bal\'azs-Attila Orb\'an, Paul O'Toole, Giuseppe Paolisso, Paolo Parini, Matteo Pedrelli, Enrico Petrillo, Philipp Poindl, Niklas Probul, Anastasia Pustozerova, Tanja \v{S}ar\v{c}evi\'c, Lukas Weilguny, Jan Baumbach, Andreas Maier
arXiv:2609.24718v1 Announce Type: new
Abstract: While a centralized approach involving patient consent to collect and analyze data centrally would theoretically offer the best data quality and predic...
By Anne-Christin Hauschild, Amirreza Aleyasin, Nils H. Beyer, Lisa Fricke, Jonas H\"ugel, Maryam Moradpour, Anh-Tien Nguyen, Youngjun Park, Sophia Rheinl\"ander, Tim Beissbarth, Elisabeth Hessmann, Martin Middeke, Matthias Lauth, Maximilian Reichert, Ulrich Sax