Product
Train on sensitive data. Keep it where it lives.
Federated learning lets regulated teams train without centralizing raw records. The coordinator, privacy knobs, and monitoring surface are built to be audited.
A federated coordinator manages rounds, node compatibility, and aggregation strategy in the repo.
Weighted aggregation produces a global update without centralizing raw records.
DP noise and privacy-budget tracking are modeled so operators can reason about tradeoffs.
DICOM and financial workflows exist in the platform codebase for regulated programs.
The point is defensibility: privacy posture, round evidence, and clear operational knobs.
Local data stays local
Updates only
Privacy posture recorded
Aggregation runs with privacy settings and produces an auditable round record. Real edge deployment is external.
- Train locally: Edge nodes compute updates without shipping raw data.
- Aggregate centrally: Coordinator collects updates and aggregates via federated averaging.
- Apply privacy: DP noise and budget tracking record the privacy posture for each round.
- Monitor: A dashboard surfaces node status, rounds, and privacy metrics.
Coordinator logic, DP modeling, and dashboard surfaces exist in the repo. Customer pilots and on-prem edge deployments are external.