Privacy Preserving Federated Learning as a Service

Training Privacy Preserving Federated Learning Models using Globus and funcX.


Learn how to start an FL experiment using APPFLx.

PALISADE-X 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 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

    Argonne Privacy Preserving Federated Learning Framework.