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.
| Original language | English |
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| Title of host publication | Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 |
| Editors | Wei Ding, Chang-Tien Lu, Fusheng Wang, Liping Di, Kesheng Wu, Jun Huan, Raghu Nambiar, Jundong Li, Filip Ilievski, Ricardo Baeza-Yates, Xiaohua Hu |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4132-4138 |
| Number of pages | 7 |
| ISBN (Electronic) | 9798350362480 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE International Conference on Big Data, BigData 2024 - Washington, United States Duration: Dec 15 2024 → Dec 18 2024 |
Publication series
| Name | Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 |
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Conference
| Conference | 2024 IEEE International Conference on Big Data, BigData 2024 |
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| Country/Territory | United States |
| City | Washington |
| Period | 12/15/24 → 12/18/24 |
Funding
This work was supported by the Office of Advanced Scientific Computing Research and performed at the Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC for the US Department of Energy under Contract No. DE-AC05-00OR22725. A portion of this research] used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. This work benefited from the use of the SasView application, originally developed under NSF award DMR-0520547. SasView contains code developed with funding from the European Union's Horizon 2020 research and innovation programme under the SINE2020 project, grant agreement No 654000. This research used resources from the ORNL Research Cloud Infrastructure at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725
Keywords
- Distributed Data
- Federated Machine Learning
- Privacy
- Scientific Ecosystems
- Security