Work

Differentially private federated learning with Laplacian smoothing

Year: 2024

Type: article

Source: Applied and Computational Harmonic Analysis

DOI: 10.1016/j.acha.2024.101660

Authors: Zhicong Liang, Bao Wang, Quanquan Gu, Stanley Osher, Yuan Yao

Institutions: Hong Kong University of Science and Technology, University of Utah, University of California, Los Angeles

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Energy, National Natural Science Foundation of China, Research Grants Council, University Grants Committee, Microsoft Research Asia, Office of Naval Research, Air Force Office of Scientific Research

Awards: Collaborative Research: Algorithms, Theory, and Validation of Deep Graph Learning with Limited Supervision: A Continuous Perspective, G1555486517, Robust Statistical Learning via Generative Adversarial Networks, Collaborative Research: Differential Equations Motivated Multi-Agent Sequential Deep Learning: Algorithms, Theory, and Validation, G4886661804, G4997068643, G5121974832, G5157887374, G7020257985, G8331043402, RELIABLE, SCALABLE, AND DATA-EFFICIENT RANDOMIZED GRAPH NEURAL NETWORKS FOR NEURAL COMBINATORIAL OPTIMIZATION WITH SCIENTIFIC APPLICATIONS, Social Choice, Crowdsourced Ranking, and Hodge Theory, Collaborative Research: ATD: Fast Algorithms and Novel Continuous-depth Graph Neural Networks for Threat Detection

OpenAlex ID: W4396696559

FWCI: 1.213

Cites: 98

Cited by: 26

Related to: 10