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:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.
By Rojalini Tripathy, Padmalochan Bera, Shreya Ghosh, Rajkumar Buyya
Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data. However, real-world healthcare federations are often characterised not only by non-IID data, but also by heterogeneous clinical objectives and partially overlapping feature spaces.
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
arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.
By Sota Sugawara, Yuji Kawamata, Akihiro Toyoda, Tomoru Nakayama, Yukihiko Okada
The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI. Federated learning (FL) is a feasible approach to overcome these challenges.
arXiv:2607. 08219v1 Announce Type: cross Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI.
By Junbin Mao, Xu Tian, Jianchun Zhu, Ludi Li, Jin Liu
arXiv:2607. 08219v2 Announce Type: replace-cross Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI.
By Junbin Mao, Xu Tian, Jianchun Zhu, Ludi Li, Jin Liu
arXiv:2606. 04338v1 Announce Type: new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis.
By Xixi Tian, Di Wu, Xiang Liu, Yiziting Zhu, Yujie Li, Xin Shu, Bin Yi
The paper introduces MACD, a Multi-Agent Clinical Diagnosis framework that enables large language models to self‑learn clinical knowledge through a multi‑agent pipeline of summarization, refinement, and application. MACD is extended into a human‑AI collaborative workflow where multiple diagnostician agents consult iteratively, guided by a judge agent and human oversight. Evaluation on the MIMIC‑MACD cohort shows significant gains in diagnostic accuracy—an average 11.6 percentage‑point improvement over authoritative knowledge for open‑weight LLMs and an 18.3‑percentage‑point boost over physician‑only diagnosis in text‑only vignettes.
By Wenliang Li, Rui Yan, Xu Zhang, Li Chen, Hongji Zhu, Jing Zhao, Junjun Li, Mengru Li, Wei Cao, Zihang Jiang, Wei Wei, Kun Zhang, Shaohua Kevin Zhou
arXiv:2512. 03296v2 Announce Type: replace-cross Abstract: Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams.
By Hsiao-Ying Lu, Kwan-Liu Ma
arXiv:2608.27856v1 Announce Type: new
Abstract: Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modelin...
By Jun Bai, Ruilin Wang, Yue Li