Our research roadmap is guided by a human-centric, ethical and trustworthy approach that is fully aligned with fundamental European values. Central to this mission and roadmap is the integration of Federated Learning (FL) and Explainable AI (XAI).

On the one hand, FL enables taking advantage of the collective intelligence of multiple clients while preserving data privacy, ensuring that sensitive information remains secure and confidential.

In other words, this approach allows model training on decentralized data without compromising individual client contributions. On the other hand, XAI plays a crucial role in enhancing the transparency and interpretability of AI models.

XAI techniques provide insights into the decision-making processes of models, allowing clients to understand how collaborative insights are derived while maintaining their data's confidentiality.

Together, FL and XAI create a powerful synergy that empowers us to deliver innovative, trustworthy AI solutions that not only prioritize data privacy but also foster client’s trust through clear and interpretable insights.