Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation

by Qingguo Lü, MS, Xiaofeng Liao PhD, Huaqing Li PhD, Shaojiang Deng PhD, Yantao Li PhD

Overview

Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation provides the reader with theoretical foundations, practical guidance, and solutions to decentralized optimization problems. The book demonstrates the application of decentralized optimization algorithms to enhance communication and computational efficiency, solve large-scale datasets, maintain privacy preservation, and address challenges in complex decentralized networks. The book covers key topics such as event-triggered communication, random link failures, zeroth-order gradients, variance-reduction, Polyak’s projection, stochastic gradient, random sleep, and differential privacy. It also includes simulations and practical examples to illustrate the algorithms' effectiveness and applicability in real-world scenarios.

Product Details

ISBN-13:	9780443333378
Publisher:	Elsevier Science
Publication date:	08/13/2025
Pages:	276
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

Qingguo Lü is a Graduate Research Assistant at Southwest University, Chongqing, China, where he is currently pursuing his Ph.D. degree in Computational Intelligence and Information Processing. His research interests include privacy protection of networked systems, Distributed Optimization, Neurodynamics, and Smart Grids.

Xiaofeng Liao received the PhD degree in Circuits and Systems from the University of Electronic Science and Technology of China, Chengdu, China, in 1997. Now he is a Professor and the Dean of the College of Computer Science, Chongqing University, Chongqing. He is also a Yangtze River Scholar of the Ministry of Education of China, Beijing, China. From 1999 to 2012, he was a Professor with Chongqing University, Chongqing, China. From Jul. 2012 to Jul. 2018, he was a Professor and the Dean of the College of Electronic and Information Engineering, Southwest University, Chongqing. From Nov. 1997 to Apr. 1998, he was a Research Associate with the Chinese University of Hong Kong, Hong Kong. From Oct. 1999 to Oct. 2000, he was a Research Associate with the City University of Hong Kong, Hong Kong. From Mar. 2001 to Jun. 2001 and Mar. 2002 to Jun. 2002, he was a Senior Research Associate at the City University of Hong Kong. From Mar. 2006 to Apr. 2007, he was a Research Fellow at the City University of Hong Kong. He serves as an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, Chinese Journal of Electronics, Computer Science, Big Data Mining and Analytics, Mathematics. His current research interests include optimization and control, machine learning, privacy protection, and neural networks. He has published more than 400 research papers and 5 monographs related to computer science.

Dr. Huaqing Li is a Professor in the College of Electronic and Information Engineering, Southwest University, Chongqing, China. He received his Ph.D. in Computer Science and Technology from Chongqing University, and was a Postdoctoral Researcher at School of Electrical and Information Engineering, The University of Sydney and at the School of Electrical and Electronic Engineering, Nanyang Technological University. His main research interests include Nonlinear Dynamics and Control, Multi-Agent Systems, and Distributed Optimization. Dr. Li currently serves as a Regional Editor for Neural Computing & Applications and an Editorial Board Member for IEEE Access.

Shaojiang Deng received the PhD degree in Computer Science from Chongqing University, China in 2005. Now he is a professor of the College of Computer Science, Chongqing University, Chongqing, China. In 2007, he was a Visiting Scholar with the Institute of Applied Computer Science, Dresden University of Technology, Dresden, Germany. His research interests focus on optimization and control, information security, and decentralized algorithms. He has published more than 80 research papers and 2 monograph related to computer science.

Yantao Li received the Ph.D. degree in computer science and technology from Chongqing University, Chongqing, China, in December 2012. He is currently a tenure-track Assistant Professor with the College of Computer Science, Chongqing University, Chongqing, China. He received the Best Paper Award from IEEE Internet Computing in 2022. He was a recipient of the Outstanding Ph.D. Thesis Award, Chongqing, in 2014, and the Outstanding Master's Thesis Award, in 2011. His research interests include mobile computing and security, the Internet of Things, sensor networks, and ubiquitous computing. Prof. Li currently serves as an Associate Editor for the IEEE Internet of Things Journal (IoT-J). His main research interests include machine learning, networked control systems, and decentralized algorithm. He has published more than 40 research papers.

Keke Zhang received the MS degree in agricultural engineering and information technology from Chongqing Three Gorges University, Chongqing, China, in 2019. She is currently pursuing the PhD degree in computer science and technology at College of Computer Science, Chongqing University, Chongqing, China. Her research interests include decentralized optimization, privacy protection, and machine learning.

Table of Contents

1. Asynchronous Decentralized Algorithms for Resource Allocation in Directed Networks
2. Event-Triggered Decentralized Accelerated Algorithms for Economic Dispatch in Networks
3. Variance-Reduced Decentralized Projection Algorithms for Constrained Optimization in Networks
4. Event-Triggered Decentralized Gradient Tracking Algorithms for Stochastic Optimization in Networks
5. Differentially Private Decentralized Dual Averaging Algorithms for Online Optimization in Directed Networks
6. Differentially Private Decentralized Zeroth-Order Algorithms for Online Optimization in Dynamic Networks
7. Privacy-Preserving Decentralized Dual Averaging Push Algorithms with Correlated Perturbations
8. Privacy-Preserving Decentralized Optimal Economic Dispatch Algorithms with Conditional Noises

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From the Publisher

Provides the reader with theoretical foundations, practical guidance, and problem-solving approaches to decentralized optimization