Federated Learning for Multi-Institution Oncology Collaboration
Multiple hospitals collaborate to train oncology models without sharing patient data, using federated learning infrastructure.
Fed-BioMed is an open-source software platform designed to support secure collaboration in healthcare.
It helps healthcare institutions and research teams collaborate on distributed data while ensuring that:
Fed-BioMed is developed for real-world healthcare environments, where governance, security, interoperability, and operational constraints are essential.
More about Fed-BioMed ➤ Explore how it works ➤Fed-BioMed is structured around four core principles.
Security mechanisms are integrated into the framework to protect communications, models, and data exchanges. The goal is to support collaboration without weakening institutional safeguards.
Each participating institution retains authority over its involvement. Experiments can be approved, reviewed, monitored, and interrupted according to local policies and responsibilities.
The framework is designed for practical use in research environments with a simple graphical interface. It supports interactive experimentation while remaining compatible with common machine learning tools.
Fed-BioMed is designed to let organizations set up and operate a federated node quickly, with minimal technical overhead and without requiring dedicated infrastructure teams.
Fed-BioMed is involved in multi-institution research initiatives and hospital collaborations. It is used in real healthcare environments where collaboration must respect institutional responsibility.