Decentralized Optimization in Networks Algorithmic Efficiency and Privacy Preservation

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

• Authors: Qingguo Lü, Liao Xiaofeng, Huaqing Li, Shaojiang Deng, Yantao Li, Keke Zhang, null Lu, Lu, Qingguo Lu
• Publisher: ELSEVIER SCIENCE BV
• Content: Book
• Product form: Paperback / Softback
• Publication date: 01.08.2025
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > Hardware
• EAN: 9780443333378
• ISBN: 978-0-443-33337-8
• Pages: 276
• Subjects: COMPUTERS / General, COMPUTERS / Networking / General, COMPUTERS / Hardware / General, COMPUTERS / Operating Systems / General, computer hardware, Operating systems, Computer architecture & logic design, Computer networking & communications, COMPUTERS / Computer Architecture, Computer architecture and logic design, Computer networking and communications, Privacy Preservation, Networked Control Systems, Communication efficiency, decentralized optimization, Computation efficiency