A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment – a Technical Case Report
Introduction: Ideally, centrally collecting and analyzing patient data with appropriate consent would provide optimal data quality and predictive performance; however, this approach is often not feasible in practice. Federated Learning (FL) or Federated Artificial Intelligence architectures have shown [for full text, please go to the a.m. URL]
Authors
- Max Reichert (ORCID: https://orcid.org/0009-0003-8352-7288)
- Ulrich Sax (ORCID: https://orcid.org/0000-0002-8188-3495)
- Tim Beißbarth (ORCID: https://orcid.org/0000-0001-6509-2143)
- Matthias Lauth (ORCID: https://orcid.org/0000-0001-5922-5384)
- Jonas Hügel (ORCID: https://orcid.org/0000-0002-4183-1287)
- Anne-Christin Hauschild (ORCID: https://orcid.org/0000-0002-7499-4373)
- Sophia Rheinländer (ORCID: https://orcid.org/0000-0002-5406-4427)
- Youngjun Park (ORCID: https://orcid.org/0000-0002-4254-2268)
- Maryam Moradpour (ORCID: https://orcid.org/0009-0001-0906-5842)
- Nils Beyer
- Anh Tien Nguyen
- Martin Middeke
- Lisa Fricke
- Amirreza Aleyasin
- Elisabeth Hessmann
Institutions
- Philipps University of Marburg (DE)
- Justus-Liebig-Universität Gießen (DE)
- TUM Klinikum (DE)
- Universitätsmedizin Göttingen (DE)
- Institute of Clinical Cancer Research (DE)
- Max Planck Institute for Biology of Ageing (DE)
- Technical University of Munich (DE)
- University of Göttingen (DE)
Publication Details
- Journal
- German Medical Science (German Research Foundation)
- Published
- 2026-04-01
- DOI
- https://doi.org/10.3205/25gmds198
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 0.00