Tailoring Gradient Methods for Differentially-Private Distributed Optimization

2 February 2022

Papers citing "Tailoring Gradient Methods for Differentially-Private Distributed Optimization"

8 / 8 papers shown

Title
Dyn-D $^2$ P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Zehan Zhu Yan Huang Xin Wang Shouling Ji Jinming Xu 28 0 0 10 May 2025
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds Zehan Zhu Yan Huang Xin Wang Jinming Xu 51 0 0 04 May 2024
Decentralized Nonconvex Optimization with Guaranteed Privacy and Accuracy Yongqiang Wang Tamer Basar 26 21 0 14 Dec 2022
A Robust Dynamic Average Consensus Algorithm that Ensures both Differential Privacy and Accurate Convergence Yongqiang Wang 28 4 0 14 Nov 2022
Ensure Differential Privacy and Convergence Accuracy in Consensus Tracking and Aggregative Games with Coupling Constraints Yongqiang Wang 30 3 0 28 Oct 2022
Decentralized Stochastic Optimization with Inherent Privacy Protection Yongqiang Wang H. Vincent Poor 24 37 0 08 May 2022
Privacy preserving distributed optimization using homomorphic encryption Yang Lu Minghui Zhu 38 161 0 01 May 2018
Distributed Event Localization via Alternating Direction Method of Multipliers Chunlei Zhang Yongqiang Wang 31 27 0 13 Jul 2016