To translate research findings into medical practice, especially in the context of federated learning (FL), we need scalable and privacy-preserving infrastructures that enable cross-institutional collaboration without compromising data confidentiality. It is crucial that the models are fair, interpretable and generalizable across patient populations. This requires overcoming challenges such as non-IID data, bias in clinical decision making and the interpretability of AI predictions. In addition, adaptation to ethical standards and legal frameworks (e.g. GDPR) is essential for clinical use.

Sensitive health data can be protected through federated learning, where data remains decentralized and only model updates are shared. To further strengthen data privacy, techniques such as differential privacy, causal modeling and influence function analysis can be integrated. These methods prevent information leakage (e.g., membership inference attacks) while enabling robust privacy-preserving learning from high-quality datasets across institutions. Our project specifically addresses these risks by developing causal privacy preservation methods that go beyond traditional anonymization.

Trust depends on three key factors: Privacy, fairness and interpretability. Patients need reassurance that their data will not be misused, that the model treats different demographic groups equally and that clinical recommendations can be understood and explained. Our research focuses on interpretable and fair federated models that use causal inference to identify and mitigate bias and attribute predictions to relevant data sources. Transparent communication about these safeguards and a participatory approach to AI development further strengthen patient trust.