文献类型：期刊文献

英文题名：Communication-Efficient and Privacy-Preserving Algorithms for Distributed Optimization

作者：He, Wangli[1]; Yang, Shaofu[2]; Yang, Zhen[1]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; [2] School of Computer Science and Engineering, Southeast University, Nanjing, China

年份：2025

起止页码：V3:507

外文期刊名：Encyclopedia of Systems and Control Engineering

收录：EI(收录号：20260520003128)

语种：英文

外文关键词：Artificial intelligence - Data mining - Learning systems - Optimization - Privacy-preserving techniques

摘要：Distributed optimization, aimed at minimizing a finite sum of local objective functions, has garnered significant attention for its emergence in diverse fields and has been extensively studied over the past decade in areas such as decentralized learning, signal and images processing, smart grids, and multi-robot systems. However, in many existing algorithms, agents are typically required to disclose their states explicitly and implement inter-node communications at each step, which may lead to serious privacy leakage and communication burden. In scenarios with potential adversaries and limited communication capabilities among system components, addressing the need for privacy protection and enhancing communication efficiency becomes crucial. This chapter gives an introduction to the topic and some technical challenges are discussed. ? 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.