Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation - Softcover

Lu MS, Qingguo; Liao PhD, Xiaofeng; Li PhD, Huaqing; Deng PhD, Shaojiang; Li PhD, Yantao; Zhang MS, Keke

Softcover

ISBN 10: 0443333378 ISBN 13: 9780443333378

Publisher: Morgan Kaufmann, 2025

Synopsis

Decentralized algorithms are useful for solving large-scale complex optimization problems, which not only alleviate the single-point resource bottleneck problem of centralized algorithms, but also possess higher scalability. Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation provides the reader with theoretical foundations, practical guidance, and problem-solving approaches to decentralized optimization. It teaches how to apply decentralized optimization algorithms to improve optimization efficiency (communication efficiency, computational efficiency, fast convergence), solve large-scale problems (training for large-scale datasets), achieve privacy preservation (effectively counter external eavesdropping attacks, differential attacks, etc), and overcome a range of challenges in complex decentralized network environments (random sleep, random link failures, time-varying, directed, etc). It focuses on: 1) communication-efficiency: event-triggered communication, random link failures, zeroth-order gradients. 2) computation-efficiency: variance-reduction, Polyak’s projection, stochastic gradient, random sleep. 3) privacy preservation: differential privacy, edge-based correlated perturbations, conditional noises. It uses simulation results, including practical application examples, to illustrate the effectiveness and the practicability of decentralized optimization algorithms.

• Introduces the latest and advanced algorithms in decentralized optimization of networked control systems
• Proposes effective strategies for efficient execution and privacy preservation in the development of decentralized optimization algorithms
• Constructs the frameworks of convergence and complexity analysis, privacy and security proof, and performance evaluation
• Includes systematic detailed implementations on how decentralized optimization algorithms solve the problems in real world systems: smart grid systems, online learning systems, wireless sensor systems, etc
• Helps reader to develop their own novel decentralized optimization algorithms

About this title

About the Authors

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.

From the Back Cover

Decentralized algorithms are useful for solving large-scale complex optimization problems, which not only alleviate the single-point resource bottleneck problem of centralized algorithms, but also possess higher scalability. Decentralized Optimization in Networks provides the reader with theoretical foundations, practical guidance, and problem-solving approaches to decentralized optimization. It teaches how to apply decentralized optimization algorithms to improve optimization efficiency (communication efficiency, computational efficiency, fast convergence), solve large-scale problems (training for large-scale datasets), achieve privacy preservation (effectively counter external eavesdropping attacks, differential attacks, etc), and overcome a range of challenges in complex decentralized network environments (random sleep, random link failures, time-varying, directed, etc). It focuses on: 1) communication-efficiency: event-triggered communication, random link failures, zeroth-order gradients. 2) computation-efficiency: variance-reduction, Polyak’s projection, stochastic gradient, random sleep. 3) privacy preservation: differential privacy, edge-based correlated perturbations, conditional noises. It uses simulation results, including practical application examples, to illustrate the effectiveness and the practicability of decentralized optimization algorithms.

Edition details

• Publisher: Morgan Kaufmann
• Publication date: 2025
• Language: English
• ISBN 10: 0443333378
• ISBN 13: 9780443333378
• Binding: Paperback
• Edition number: 1
• Number of pages: 300