Strategies in Federated Learning for MedTech AI

Training models across sites and satisfying Predetermined Change Control Plan (PCCP) requirements, without ever moving a patient record.

Over 1,250 AI-enabled devices carry FDA authorization today, and most of them were built where the data was easy to reach. The models that could actually change patient outcomes, the ones that generalize across sites, rare conditions, and diverse populations, are stuck behind hospital walls that privacy law was built to protect.

Federated learning flips the equation: instead of moving patient data to the model, the model moves to the data. Trained locally, validated centrally, defensible to a reviewer. This white paper explains how it works, when it’s the right call, and how a well-scoped Predetermined Change Control Plan turns continuous model improvement from a regulatory liability into a planned, reviewable process.

We didn’t stop at theory. Our AI Lab built a working federated learning model at the Open Accelerator hackathon, proving the governance and the code, not just the concept.

What’s Inside:

• How federated learning trains one model without ever pooling patient data
• Where it earns its complexity, and where it’s the wrong tool for the job
• The privacy gaps secure aggregation alone won’t close
• What FDA’s PCCP guidance demands of a model built to keep learning
• Avania’s own federated learning build, from concept to working demo