Work

Project: Privacy-Preserving Federated Learning for Science

Year: 2026

Type: article

Abstract: This repository is part of a U.S. Department of Energy (DOE) Advanced Scientific Computing Research (ASCR) project focused on developing privacy-preserving federated learning approaches for scientific foundation models. The project aims to advance secure, trustworthy, and sustainable machine learning methodologies for large-scale scientific applications. ORNL Principal Investigator: Olivera Kotevska, PhD. Project Duration: 2024-2027 Website: https://github.com/ORNL/Sci_PPFL_FM

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23289296

Author: Kotevska, Olivera

Institutions: Oak Ridge National Laboratory, University of Tennessee at Knoxville

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7221451841

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