Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning

2026-09-17

AI Summary

本文针对多中心医疗数据挖掘中联邦学习框架不满足实际需求的问题，提出了一种新的解决方案FL-Net，它集成了多种功能以支持隐私保护下的协作研究。

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.

Problem

• federated learning
• multi-center medical data
• data harmonization
• disclosure control
• reproducibility

Innovation

• Federated Learning
• Data Harmonization
• Cross-study Patient Discovery
• Reproducible Federated Workflows
• Privacy-preserving