Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
cs.LG, cs.CR, cs.DC
Submitted: 2026-09-17
Updated: 2026-09-17
Comments: 69 pages, 8 figures, includes supplementary material
License: http://creativecommons.org/licenses/by/4.0/
The gist: Federated learning enables collaborative training without sharing patient-level data, but most studies remain simulations.
Terminology
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.
Sources
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Federated Optimization: Distributed Machine Learning for On-Device Intelligence
- Building Privacy-and-Security-Focused Federated Learning Infrastructure for Global Multi-Centre Healthcare Research
- A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers
- Flower: A Friendly Federated Learning Research Framework
- NVIDIA FLARE: Federated Learning from Simulation to Real-World
- Biomedical systems biology workflow orchestration and execution with PoSyMed
- Ontology-based Data Access: A Study through Disjunctive Datalog, CSP, and MMSNP
- Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging
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