This thesis investigates how federated learning can be applied to train vision-language models in the medical domain while preserving patient privacy. The work focuses on enabling multi-institutional collaboration without sharing sensitive data, supporting the development of secure and scalable AI solutions for healthcare.
Privacy-Preserving Structured Chest X-Ray Report Generation using Multimodal Large Language Models within a Federated Learning Framework
Type: MA thesis
Status: running
Date: September 29, 2025 - March 30, 2026
Supervisors: Lukas Buess, Andreas Maier