Building AI Models For Science

Privacy-Preserving Federated Learning for Science: Building Sustainable and Trustworthy Foundation Models.

GET STARTED

Learn how to start an FL experiment using APPFLx.

Our Objectives

Develop Advanced Privacy Preserving Federated Learning (APPFL) framework that implements differentially private (DP) algorithms for training federated learning (FL) models with the biomedical and scientific datasets from multiple organizations and

Integrate, deploy, and demonstrate the proposed framework with our existing secure computing and data infrastructure.

To accomplish our objectives, we created a privacy-preserving AI/ML architecture, which will allow us to validate APPFL framework with real-world, multi-modal biomedical data repositories that align with the NIH Bridge2AI pilot flagship data generation projects.

APPFL Framework

Advanced Privacy Preserving Federated Learning Framework.