Fed-BioMed is an open-source platform designed to enable collaborative analysis of sensitive data—especially in healthcare—without requiring data to be centralized. It relies on a distributed architecture where hospitals and research institutions keep data locally, contributing to privacy and compliance with regulations such as GDPR, while still participating in joint studies and analyses.

Unlike solutions focused solely on federated learning, Fed-BioMed supports multiple steps across the entire data pipeline. It includes federated discovery (to explore and identify available data across nodes), federated analytics (to run distributed statistical analyses), federated pre-processing (to prepare data locally), and federated learning (to train AI models collaboratively). This comprehensive approach allows researchers to work with distributed datasets from the exploratory phase through to advanced modeling.

With this end-to-end vision, Fed-BioMed enables secure, scalable collaboration across institutions, allowing access to distributed knowledge without compromising data confidentiality. Its goal is to accelerate biomedical research and innovation, supporting use cases such as large-scale multicenter studies and personalized medicine.