Yantao Li – författare
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3 produkter
3 produkter
Häftad, Engelska, 2025
1 573 kr
Skickas inom 10-15 vardagar
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.Introduces the latest and advanced algorithms in decentralized optimization of networked control systemsProposes effective strategies for efficient execution and privacy preservation in the development of decentralized optimization algorithmsConstructs the frameworks of convergence and complexity analysis, privacy, security proof, and performance evaluationIncludes 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 readers develop their own novel, decentralized optimization algorithms
E-bok
Engelska, 20251 988 kr
Läs direkt efter köp
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.- 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, 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 readers develop their own novel, decentralized optimization algorithms
Häftad, Engelska, 2027
1 970 kr
Kommande
Sensor-based continuous authentication has emerged as a critical approach for strengthening mobile security, enabling persistent user verification without disrupting device usage. However, the field faces significant hurdles, including limited training data, complex feature representation, environmental noise, and the strict resource constraints of mobile hardware.Deep Learning Models for Continuous Authentication on Mobile Devices provides a unified and structured treatment of data-driven continuous authentication, presenting a systematic study of sensor-based continuous authentication on mobile devices, focusing on modern machine learning and deep learning techniques. It guides readers in designing, analyzing, and deploying reliable systems that effectively balance security, robustness, and computational efficiency. Featuring data augmentation strategies for data scarcity, multi-sensor feature fusion, discriminative feature learning via two-stream CNNs, data synthesis using conditional Wasserstein GANs, lightweight networks for efficient deployment, neural architecture search for automated optimization, and neuromorphic computing with spiking neural networks,Deep Learning Models for Continuous Authentication on Mobile Devices balances methodological rigor with practical system design, offering robust solutions for real-world mobile security.Introduces representative sensor-based continuous authentication methods on mobile devices, spanning data augmentation, feature fusion, convolutional and generative models, automated architecture search, and neuromorphic learning, offering comprehensive guidance for students and researchersPresents practical strategies to address critical challenges in the field, including limited training data, inter-user behavioral variability, robustness to environmental noise and mimic behaviors, and the requirements for efficient deployment on mobile platformsIncludes systematic experimental analysis and implementation insights derived from both public and real-world datasets, helping practitioners understand the performance of continuous authentication methods in practical scenarios and design their own effective security solutions