Articles liés à Federated Learning for Wireless Networks
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- ÉditeurSpringer Verlag, Singapore
- Date d'édition2021
- ISBN 10 9811649626
- ISBN 13 9789811649622
- ReliureRelié
- Numéro d'édition1
- Nombre de pages253
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Federated Learning for Wireless Networks
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Description du livre Etat : New. Book is in NEW condition. 1.15. N° de réf. du vendeur 9811649626-2-1
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Federated Learning for Wireless Networks
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Description du livre Etat : New. New! This book is in the same immaculate condition as when it was published 1.15. N° de réf. du vendeur 353-9811649626-new
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Federated Learning for Wireless Networks by Hong, Choong Seon, Khan, Latif U., Chen, Mingzhe, Chen, Dawei, Saad, Walid, Han, Zhu [Hardcover ]
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Description du livre Hardcover. Etat : new. N° de réf. du vendeur 9789811649622
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Description du livre Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. N° de réf. du vendeur ria9789811649622_lsuk
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Federated Learning for Wireless Networks
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Description du livre Etat : New. N° de réf. du vendeur ABLIING23Apr0412070092731
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Federated Learning for Wireless Networks
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Description du livre Gebunden. Etat : 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. N° de réf. du vendeur 485171020
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Federated Learning for Wireless Networks
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Springer Nature Singapore Dez 2021
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Description du livre Buch. Etat : 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 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. 268 pp. Englisch. N° de réf. du vendeur 9789811649622
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Federated Learning for Wireless Networks
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Springer Nature Singapore
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ISBN 10 : 9811649626
ISBN 13 : 9789811649622
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Description du livre Buch. Etat : Neu. Druck auf Anfrage 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 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. N° de réf. du vendeur 9789811649622
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Federated Learning for Wireless Networks
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Description du livre Etat : New. N° de réf. du vendeur I-9789811649622
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