Privacy and security
Built for the most sensitive data environments.
Federated LearningCross-Institutional Intelligence, without Moving Data
Encrypted parameters only. Raw records stay local.
Bavel Health's Federated Learning Layer enables secure, large-scale medical research across institutions without ever moving patient data.
Enterprise-grade performance, reliability, and scalability, built for the complexity of real clinical environments..
How it works
AI models are trained locally, inside each hospital. Sensitive patient data never leaves its source. Instead of sharing raw data, only encrypted model parameters travel between the nodes, allowing collective intelligence to emerge without centralising anything.
The result is a network where every institution contributes to shared knowledge without surrendering control of what makes that knowledge possible.
Fault-tolerant algorithms
The network remains robust even when individual nodes go offline or behave unexpectedly. Clinical research does not pause because one institution has a problem.
Heterogeneous compatibility
Works across different clinical systems, datasets, and institutional protocols without requiring standardisation upfront. Institutions join as they are, not as we wish they were.
Regulatory readiness
Bavel's Federated Learning Layer is built for GDPR, EHDS, HIPAA, and EU AI Act readiness directly at the infrastructure layer, enabling cross-border clinical research and AI deployment without parallel compliance architectures.
The intelligence layer
Medical knowledge has always been siloed by institution, by country, by system. Bavel Health's Federated Learning Layer bridges those boundaries while preserving the privacy guarantees that make healthcare data worth protecting.
Hospitals contribute. Everyone learns. No patient record moves.
- Privacy-preserving model training.
- Multi-site clinical research. No data centralisation.
- Enterprise-grade reliability.