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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a comprehensive study ofFederated Learning (FL) over wireless networks. It consists ofthree main parts: (a) Fundamentals and preliminaries ofFL, (b) analysis and optimization ofFL over wireless networks, and (c) applications of wireless FL for Internet-of-Things systems. In particular, in the first part, the authors provide a detailed overview on widely-studied FL framework. In thesecond part ofthis book, theauthors comprehensively discuss three key wireless techniques including wireless resource management, quantization, and over-the-air computation tosupport thedeployment ofFL over realistic wireless networks. It also presents several solutions based onoptimization theory, graph theory and machine learning tooptimize theperformance ofFL over wireless networks. In thethird part ofthis book, theauthors introduce theuse ofwireless FL algorithms for autonomous vehicle control and mobile edge computing optimization.Machine learning and data-driven approaches have recently received considerable attention as key enablers for next-generation intelligent networks. Currently, most existing learning solutions for wireless networks rely on centralizing the training and inference processes by uploading data generated at edge devices to data centers. However, such a centralized paradigm may lead to privacy leakage, violate the latency constraints of mobile applications, or may be infeasible due to limited bandwidth or power constraints of edge devices. To address these issues, distributing machine learning at the network edge provides a promising solution, where edge devices collaboratively train a shared model using real-time generated mobile data. The avoidance of data uploading to a central server not only helps preserve privacy but also reduces network traffic congestion as well as communication cost. Federated learning (FL) is one of most important distributed learning algorithms. In particular, FL enables devices to train a shared machine learning model while keeping data locally. However, in FL, training machine learning models requires communication between wireless devices and edge servers over wireless links. Therefore, wireless impairments such as noise, interference, and uncertainties among wireless channel states will significantly affect the training process and performance of FL. For example, transmission delay can significantly impact the convergence time of FL algorithms. In consequence, it is necessary to optimize wireless network performance for the implementation of FL algorithms.This book targets researchers and advanced level students in computer science and electrical engineering. Professionals working in signal processing and machine learning will also buy this book. 179 pp. Englisch.…

Communication Efficient Federated Learning for Wireless Networks

Mingzhe Chen

Lingua: Inglese

Editore: Springer, Berlin, Springer Nature Switzerland, Springer, 2024

3031512650 / 9783031512650

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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a comprehensive study ofFederated Learning (FL) over wireless networks. It consists ofthree main parts: (a) Fundamentals and preliminaries ofFL, (b) analysis and optimization ofFL over wireless networks, and (c) applications of wireless FL for Internet-of-Things systems. In particular, in the first part, the authors provide a detailed overview on widely-studied FL framework. In thesecond part ofthis book, theauthors comprehensively discuss three key wireless techniques including wireless resource management, quantization, and over-the-air computation tosupport thedeployment ofFL over realistic wireless networks. It also presents several solutions based onoptimization theory, graph theory and machine learning tooptimize theperformance ofFL over wireless networks. In thethird part ofthis book, theauthors introduce theuse ofwireless FL algorithms for autonomous vehicle control and mobile edge computing optimization.Machine learning and data-driven approaches have recently received considerable attention as key enablers for next-generation intelligent networks. Currently, most existing learning solutions for wireless networks rely on centralizing the training and inference processes by uploading data generated at edge devices to data centers. However, such a centralized paradigm may lead to privacy leakage, violate the latency constraints of mobile applications, or may be infeasible due to limited bandwidth or power constraints of edge devices. To address these issues, distributing machine learning at the network edge provides a promising solution, where edge devices collaboratively train a shared model using real-time generated mobile data. The avoidance of data uploading to a central server not only helps preserve privacy but also reduces network traffic congestion as well as communication cost. Federated learning (FL) is one of most important distributed learning algorithms. In particular, FL enables devices to train a shared machine learning model while keeping data locally. However, in FL, training machine learning models requires communication between wireless devices and edge servers over wireless links. Therefore, wireless impairments such as noise, interference, and uncertainties among wireless channel states will significantly affect the training process and performance of FL. For example, transmission delay can significantly impact the convergence time of FL algorithms. In consequence, it is necessary to optimize wireless network performance for the implementation of FL algorithms.This book targets researchers and advanced level students in computer science and electrical engineering. Professionals working in signal processing and machine learning will also buy this book. 179 pp. Englisch.…

Communication Efficient Federated Learning for Wireless Networks

Mingzhe Chen|Shuguang Cui

Lingua: Inglese

Editore: Springer Nature Switzerland, 2024

3031512650 / 9783031512650

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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a comprehensive and systematic book on design of federated learningProvides key approaches for optimizing performance of federated learningDemonstrates effective applications of federated learning in wireless networksMingzhe .…

Federated Learning for Wireless Networks

Lingua: Inglese

Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2022

9811649650 / 9789811649653

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Da: moluna, Greven, Germaniamoluna

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference pr.…

Federated Learning for Wireless Networks

Lingua: Inglese

Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2021

9811649626 / 9789811649622

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Da: moluna, Greven, Germaniamoluna

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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, s.…

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Communication Efficient Federated Learning for Wireless Networks [Hardcover] [Feb 20, 2024] Chen, Mingzhe and Cui, Shuguang

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