AIME-WS 2026

Table of Contents

• PDF
  
• Privacy-Preserving Federated Learning and Analytics with FLAME 1-5 PDF
  Marius Herr, Hammam Abu Attieh, Peter Placzek, Mehmed Halilovic, Bruce Schultz, David Hieber, Alexander Röhl, Mehrshad Jaberansary, Paul Brassel, Max Schaible, Toralf Kirsten, Fabian Prasser, Oliver Kohlbacher
• On the Importance of Centralized Learning in Health Research 6-14 PDF
  Michael Wolfson
• Dynamic Trust-Aware Federated Aggregation for Privacy-Aware Multicentric Healthcare AI 15-22 PDF
  Elarbi Badidi, Omar Elharrouss
• Can Zero-Shot LLMs Predict Child Malnutrition? A Fairness and Temporal Robustness Study 23-28 PDF
  Muhammad Ashad Kabir, Md Ahshanul Haque
• A Graph Attention Transformer Framework for Integrating Socioeconomic and Environmental Data in Disease Prevalence Modeling 29-36 PDF
  Maksim I. Pirozhkov, Pavel A. Vedeneev
• Machine Learning for Menstrual Recovery Prediction Using Synthetic Wearable Data 37-41 PDF
  Lillian Shen, Pouria Mortezaagha, Arya Rahgozar
• The Selection Bottleneck Versus Attention Diffusion: Quantifying Coverage–Latency Tradeoffs in Guideline-Grounded Clinical QA 42-46 PDF
  Ahmed Issaoui, Jennifer Dawson, Roger Zemek, Arya Rahgozar