9789811649622 - federated learning for wireless networks de hong, choong seon; khan, latif u.; chen, mingzhe (15 resultados)
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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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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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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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 222,76
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Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
Federated Learning for Wireless Networks
Hong, Choong Seon/ Khan, Latif U./ Chen, Mingzhe/ Chen, Dawei/ Saad, Walid
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 265 pages. 9.25x6.10x0.83 inches. In Stock.
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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Librería: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, AlemaniaBUCHSERVICE / ANTIQUARIAT Lars Lutzer
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Hardcover. Condición: gut. 2021. Federated Learning for Wireless Networks In deutscher Sprache. pages.
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condición: new. Questo è un articolo print on demand.
Federated Learning for Wireless Networks
Hong, Choong Seon|Khan, Latif U.|Chen, Mingzhe|Chen, Dawei|Saad, Walid|Han, Zhu
Idioma: Inglés
Editorial: Springer, Berlin|Springer Nature Singapore|Springer, 2021
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Librería: moluna, Greven, Alemaniamoluna
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Gebunden. Condición: 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, cove…ring resource optimization, incentive mechanism, s.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 171,19
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 a…nd 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. 268 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 185,59
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Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 in…ference 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.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 171,19
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 i…nference 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 268 pp. Englisch.
Federated Learning for Wireless Networks
Hong, Choong Seon; Khan, Latif U.; Chen, Mingzhe; Chen, Dawei; Saad, Walid; Han, Zhu
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 230,53
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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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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 236,14
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Condición: New. PRINT ON DEMAND.
