PALISADE-X

Privacy-preserving analysis and learning in secure and distributed enclaves and exascale systems funded by DOE ASCR

Overall Objectives

Objectives

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.

Updates

Repository for the Argonne Privacy Preserving Federated Learning Framework: https://github.com/APPFL/APPFL
Applications where APPFL is being used: https://github.com/APPFL/appfl_applications