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A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment – a Technical Case Report

Year: 2026

Type: conference-paper

Abstract: 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]

Source: German Medical Science (German Research Foundation)

DOI: 10.3205/25gmds198

Authors: Hauschild, Anne-Christin, Aleyasin, Amirreza, Beyer, Nils, Fricke, Lisa, Hügel, Jonas, Moradpour, Maryam, Nguyen, Anh Tien, Park, Youngjun, Rheinländer, Sophia, Beißbarth, Tim, Hessmann, Elisabeth, Lauth, Matthias, Middeke, Martin, Reichert, Max, Sax, Ulrich

Institutions: Justus-Liebig-Universität Gießen, Universitätsmedizin Göttingen, Campus-Institut Data Science (CIDAS), University of Göttingen, TUM Klinikum, Technical University of Munich, Max Planck Institute for Biology of Ageing, Philipps University of Marburg, German Cancer Research Center, Deutsches Konsortium für Translationale Krebsforschung

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7146938136

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