Work

Privacy-Preserving Federated Learning for Science: Challenges and Research Directions

Year: 2024

Type: conference-paper

Source: IEEE International Conference on Big Data (BigData)

DOI: 10.1109/bigdata62323.2024.10825853

Authors: Kibaek Kim, Krishnan Raghavan, Olivera Kotevska, Matthieu Dorier, Ravi Madduri, Minseok Ryu, Todd Munson, Rob Ross, Thomas Flynn, Ai Kagawa, Byung-Jun Yoon, Christian Engelmann, Farzad Yousefian

Institutions: Argonne National Laboratory, Oak Ridge National Laboratory, Arizona State University, Brookhaven National Laboratory, Rutgers, The State University of New Jersey

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funders: U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research

OpenAlex ID: W4406460289

FWCI: 1.468

Cites: 29

Cited by: 5

Related to: 10