SP Technologies successfully designed and implemented a groundbreaking Federated Learning Platform for collaborative medical research, involving five major Swiss hospitals. This 6-month project focused on enabling data-driven medical research while ensuring the highest standards of patient data privacy and security.
Key achievements:
-
Data Platform: We built a distributed data infrastructure that allowed hospitals to collaborate on machine learning models without sharing raw patient data, using TensorFlow Federated for secure, decentralized model training.
-
Data Privacy: Implemented advanced privacy-preserving techniques, including differential privacy and secure multi-party computation, to protect individual patient data while allowing for meaningful aggregate analysis.
-
Blockchain Integration: Utilized blockchain technology to create an immutable audit trail of all data access and model updates, ensuring transparency and trust among participating institutions.
The platform enabled researchers to train AI models on a vastly larger and more diverse dataset than ever before, while fully complying with GDPR and Swiss data protection regulations. It supported various types of medical studies, from rare disease research to drug efficacy analysis.
This implementation resulted in a 200% increase in the volume of data available for collaborative research, while maintaining zero data breaches. The project accelerated several ongoing medical studies, with one rare disease study reporting a 40% reduction in time-to-insight.
The success of this platform positioned Switzerland at the forefront of privacy-preserving medical research, opening new possibilities for international collaboration in healthcare AI.