Chen mingzhe (44 risultati)
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Federated Learning for Wireless Networks
Hong, Choong Seon; Khan, Latif U.; Chen, Mingzhe; Chen, Dawei; Saad, Walid; Han, Zhu
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Federated Learning for Wireless Networks
Hong, Choong Seon; Khan, Latif U.; Chen, Mingzhe; Chen, Dawei; Saad, Walid; Han, Zhu
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Federated Learning for Wireless Networks
Hong, Choong Seon; Khan, Latif U.; Chen, Mingzhe; Chen, Dawei; Saad, Walid
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Federated Learning for Wireless Networks
Hong, Choong Seon; Khan, Latif U.; Chen, Mingzhe; Chen, Dawei; Saad, Walid
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Federated Learning for Wireless Networks
Hong, Choong Seon; Khan, Latif U.; Chen, Mingzhe; Chen, Dawei; Saad, Walid
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Federated Learning for Wireless Networks
Hong, Choong Seon; Khan, Latif U.; Chen, Mingzhe; Chen, Dawei; Saad, Walid
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Taschenbuch. Condizione: Neu. Communication Efficient Federated Learning for Wireless Networks | Mingzhe Chen (u. a.) | Taschenbuch | Wireless Networks | xi | Englisch | 2025 | Springer | EAN 9783031512681 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]spri…nger[dot]com | Anbieter: preigu.
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Condizione: New. 1st ed. 2024 edition NO-PA16APR2015-KAP.
Federated Learning for Wireless Networks
Choong Seon Hong, Latif U. Khan, Zhu Han, Dawei Chen, Walid Saad, Mingzhe Chen
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Da: Buchpark, Trebbin, GermaniaBuchpark
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Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 268 | Sprache: Englisch | Produktart: Bücher | 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 processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. 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, security and privacy. It also presents several solutions based on optimizationtheory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.
Federated Learning for Wireless Networks
Hong, Choong Seon; Khan, Latif U.; Chen, Mingzhe; Chen, Dawei; Saad, Walid; Han, Zhu
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Federated Learning for Wireless Networks
Hong, Choong Seon; Khan, Latif U.; Chen, Mingzhe; Chen, Dawei; Saad, Walid; Han, Zhu
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Federated Learning for Wireless Networks
Hong, Choong Seon/ Khan, Latif U./ Chen, Mingzhe/ Chen, Dawei/ Saad, Walid/ Han, Zhu
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Paperback. Condizione: Brand New. 265 pages. 9.25x6.10x0.56 inches. In Stock.
Federated Learning for Wireless Networks
Hong, Choong Seon/ Khan, Latif U./ Chen, Mingzhe/ Chen, Dawei/ Saad, Walid
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Federated Learning for Wireless Networks Choong Seon Hong, Latif U. Khan, Zhu Han, Dawei Chen, Walid Saad, Mingzhe Chen
Choong Seon Hong, Latif U. Khan, Zhu Han, Dawei Chen, Walid Saad, Mingzhe Chen
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Da: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, GermaniaBUCHSERVICE / ANTIQUARIAT Lars Lutzer
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Hardcover. Condizione: gut. 2021. Federated Learning for Wireless Networks In deutscher Sprache. pages.
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer, Berlin, Springer Nature Switzerland, Springer, 2025
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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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.
Lingua: Inglese
Editore: Springer, Berlin, Springer Nature Switzerland, Springer, 2024
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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) applica…tions 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.
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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. 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 .
