Privacy Preserving Federated Learning for Advanced Scientific Ecosystems

Year: 2024

Type: conference-paper

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.

Source: IEEE International Conference on Big Data (BigData)

DOI: 10.1109/bigdata62323.2024.10825977

Authors: Rick Archibald, Addi Malviya Thakur, Marshall McDonnell, Gregory Cage, Cody Stiner, Lance Drane, Paul Laiu, Michael J. Brim, Mathieu Doucet, William T. Heller, Ryan Coffee

Institutions: Oak Ridge National Laboratory, SLAC National Accelerator Laboratory

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Energy, Battelle, UT-Battelle, European Commission, Office of Science, Division of Materials Research, Advanced Scientific Computing Research, Oak Ridge National Laboratory

OpenAlex ID: W4406458201