As healthcare rapidly enters the age of data-driven intelligence, one question keeps resurfacing: How can we innovate with data without compromising privacy?
At HealthTech Forward 2025, the TRUMPET Project provided a powerful answer during its final event, titled “Sharing without Sharing: the TRUMPET federated approach for privacy-preserving AI computation.” Hosted in the vibrant innovation hub of Barcelona, the session brought together technical experts, legal specialists, and clinical innovators to demonstrate how Federated Learning (FL) is reshaping biomedical AI while fully respecting patient confidentiality.
The session will take place on December 3rd – at 15.30 CET
Don’t miss the chance to explore the future of privacy-preserving AI in healthcare. Secure your spot at HealthTech Forward 2025 and join the TRUMPET final event.
The Promise of Federated Learning: the solutions developed by TRUMPET Consortium discussed during the event.
Federated Learning (FL) is transforming collaborative health research by making it possible to train AI models on decentralized datasets—without ever transferring sensitive patient information.
Instead of pooling data in a single repository, algorithms travel to each data source, learn locally, and aggregate insights anonymously. This model:
- Upholds GDPR principles of data minimisation and confidentiality
- Maintains full control of data on hospital premises
- Enables research teams to work together at scale without exposing personal health information
The TRUMPET project, funded by the European Union, has taken this paradigm even further by addressing both the technical and regulatory challenges behind secure, privacy-preserving computation. Its federated framework paves the way for trustworthy, large-scale biomedical AI innovation across Europe.