- By:
- Archibald, Richard K; Malviya Thakur, Aditi A; McDonnell, Marshall T; Cage, Gregory E; Stiner, Justin C; Drane, Lance T; Laiu, Ming Tse P; Brim, Michael J; Doucet, Mathieu ; Heller, William T; Coffee, Ryan
- Page Number:
- 4132-4138
- Book Title:
- 2024 IEEE International Conference on Big Data (BigData)
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 2024 IEEE International Conference on Big Data (BigData)
- Conference Location:
- Washington, District of Columbia, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/BigData62323.2024.10825977
Abstract
We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.
