DP-GSGLD: A Bayesian optimizer inspired by differential privacy defending against privacy leakage in federated learning-Reference-Cited by-同舟云学术

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DP-GSGLD: A Bayesian optimizer inspired by differential privacy defending against privacy leakage in federated learning

Published:2024-07 Issue: Volume:142 Page:103839
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Yang Chengyi^ORCID,Jia Kun,Kong Deli,Qi Jiayin,Zhou Aimin

Funder

Beijing University of Posts and Telecommunications

National Natural Science Foundation of China

East China Normal University

Publisher

Elsevier BV

Reference68 articles.

1. Deep learning with differential privacy;Abadi,2016

2. Bayesian posterior sampling via stochastic gradient Fisher scoring;Ahn,2012

3. Kymatio: scattering transforms in python;Andreux;J. Mach. Learn. Res.,2020

4. Wasserstein generative adversarial networks;Arjovsky,2017

5. Private empirical risk minimization: efficient algorithms and tight error bounds;Bassily,2014

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1. An intelligent diagnostic model for industrial equipment with privacy protection;Computers & Security;2024-11

2. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024

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