.png){kind=icon}
返回
Differentially private projected gradient tracking for distributed constrained optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2026.112851.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper investigates a distributed constrained optimization problem with privacy concerns over a multi-agent network, where each agent holds a private objective function and seeks a global optimizer subject to a global closed convex set of constraints in a distributed and privacy-preserving manner. To address this problem, a differentially private projected gradient tracking algorithm is proposed. The main idea is to introduce indirect projection in gradient tracking to handle the global constraints and to decompose the gradient tracking state into two sub-states, with the shared sub-state being perturbed by decaying Laplace noise, to establish differential privacy (DP). Two time scales and a lazy update rule are employed to facilitate the convergence analysis. It is demonstrated that the proposed algorithm simultaneously preserves linear convergence rates and ϵ-DP without requiring bounded gradients. Finally, numerical simulations are presented to verify the theoretical results.
Keyword:
distributed optimization
differential privacy
gradient tracking
constrained optimization
multi-agent network
期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
机构
引用论文
分享收藏
分享收藏Privacy-Preserving Distributed Optimisation using Stochastic PDMM.png)
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)IF0
PREAI
分享收藏
查看更多内容