(Start: Oct 1, 2021)
The main objectives of PALISADE-X are to:
develop Argonne PPFL (APPFL) framework that implements differentially private (DP) algorithms for training federated learning (FL) models with the biomedical 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 will create 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.