Privacy Preservation for Distributed Nonsmooth Constrained Optimization Based on Pseudo-Subgradient
作者单位：Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing Advanced Innovation Center for Intelligent Robots and Systems (Beijing Institute of Technology) Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education
会议名称：《第37届中国控制会议》
会议日期：2018年
学科分类：08[工学] 0839[工学-网络空间安全] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by Projects of Major International(Regional)Joint Research Program NSFC(Grant no.61720106011) NSFC(Grant no.61603378,61621063,61781340258) Fundamental Research Funds for the China Central Universities of USTB(No.FRF-TP-17-088A1) China Postdoctoral Science Foundation(2017M620020)
关 键 词：Distributed nonsmooth convex optimization privacy preservation set constraints pseudo-subgradients
摘 要：In this paper, we investigate a privacy preservation design in the distributed nonsmooth convex optimization with set constraints. To solve the distributed optimization problem while preserving the privacy, we use pseudo-subgradients involved with（non-integrable） set-valued functions. Based on pseudo-subgradients, we propose distributed nonsmooth optimization algorithms with keeping subgradient information confidential. Then we prove the correctness and convergence of the distributed privacy preservation optimization algorithms to achieve the exact solution of the original optimization problem.