Compressed Differentially Private Distributed Optimization with Linear Convergence

Autor:	Xie, Antai, Yi, Xinlei, Wang, Xiaofan, Cao, Ming, Ren, Xiaoqiang
Rok vydání:	2023
Předmět:	Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	This paper addresses the problem of differentially private distributed optimization under limited communication, where each agent aims to keep their cost function private while minimizing the sum of all agents' cost functions. In response, we propose a novel Compressed differentially Private distributed Gradient Tracking algorithm (CPGT). We demonstrate that CPGT achieves linear convergence for smooth and strongly convex cost functions, even with a class of biased but contractive compressors, and achieves the same accuracy as the idealized communication algorithm. Additionally, we rigorously prove that CPGT ensures differential privacy. Simulations are provided to validate the effectiveness of the proposed algorithm. Comment: To appear in IFAC WC 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.01779 Zobrazit plný text záznamu View this record from Arxiv