Enhancing Trust by a Keycloak-Flower Integration for Federated Machine Learning.

Journal: Studies in health technology and informatics
Published Date:

Abstract

Since its introduction, federated learning (FL) has attracted a lot of attention in the medical field, but its actual application in healthcare organisations remains limited. Flower is a leading FL framework known for its good documentation and wide application. To close security gaps, we propose to integrate Keycloak with gRPC and Flower to improve identity and access management. We have developed a lightweight Python module that integrates both and also validates the client's code with the server before execution. The system has been tested in a simple prototype, but further work and security testing is required for a complex evaluation.

Authors

  • Matthaeus Morhart
    Digital Medicine, University Hospital of Augsburg, Augsburg, Germany.
  • Johanna Schwinn
    Digital Medicine, University Hospital of Augsburg, Augsburg, Germany.
  • Seyedmostafa Sheikhalishahi
    University of Trento, Trento, Italy.
  • Michael Wellnhofer
    Digital Medicine, University Hospital of Augsburg, Augsburg, Germany.
  • Ludwig Christian Hinske
    Institute for Digital Medicine, University Hospital Augsburg, Augsburg, Germany.
  • Mathias Kaspar
    University Hospital of Würzburg, Comprehensive Heart Failure Center.