東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Federated learning for wireless networks

Hong, Choong Seon.

FindBook

Google Book

Amazon

博客來

Federated learning for wireless networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Federated learning for wireless networks/ by Choong Seon Hong ... [et al.].
其他作者:	Hong, Choong Seon.
出版者:	Singapore :Springer Nature Singapore : : 2021.,
面頁冊數:	xii, 253 p. :ill., digital ;24 cm.
內容註:	Part 1 Fundamentals and Background -- 1 Introduction -- 2 Fundamentals of Federated Learning -- Part 2 Wireless Federated Learning: Design and Analysis 3 Resource Optimization for Wireless Federated Learning -- 4 Incentive Mechanisms for Federated Learning -- 5 Security and Privacy -- 6 Unsupervised Federated Learning -- Part 3 Federated Learning Applications in Wireless Networks -- 7 Wireless Virtual Reality -- 8 Vehicular Networks and Autonomous Driving Cars -- 9 Smart Industries and Intelligent Reflecting Surfaces.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-981-16-4963-9
ISBN:	9789811649639

Federated learning for wireless networks
Federated learning for wireless networks[electronic resource] /by Choong Seon Hong ... [et al.]. - Singapore :Springer Nature Singapore :2021. - xii, 253 p. :ill., digital ;24 cm. - Wireless networks,2366-1445. - Wireless networks..

Part 1 Fundamentals and Background -- 1 Introduction -- 2 Fundamentals of Federated Learning -- Part 2 Wireless Federated Learning: Design and Analysis 3 Resource Optimization for Wireless Federated Learning -- 4 Incentive Mechanisms for Federated Learning -- 5 Security and Privacy -- 6 Unsupervised Federated Learning -- Part 3 Federated Learning Applications in Wireless Networks -- 7 Wireless Virtual Reality -- 8 Vehicular Networks and Autonomous Driving Cars -- 9 Smart Industries and Intelligent Reflecting Surfaces.

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 optimization theory, 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.

ISBN: 9789811649639

Standard No.: 10.1007/978-981-16-4963-9doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Federated learning for wireless networks
LDR:03740nmm a2200349 a 4500 001 2301499
003 DE-He213
005 20220502121000.0
006 m d
007 cr nn 008maaau
008 230409s2021 si s 0 eng d
020 $a 9789811649639 $q (electronic bk.)
020 $a 9789811649622 $q (paper)
024 7 $a 10.1007/978-981-16-4963-9 $2 doi
035 $a 978-981-16-4963-9
035 $a 2301499
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UKN $2 bicssc
072 7 $a COM075000 $2 bisacsh
072 7 $a UKN $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .F293 2021
245 0 0 $a Federated learning for wireless networks $h [electronic resource] / $c by Choong Seon Hong ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2021.
300 $a xii, 253 p. : $b ill., digital ; $c 24 cm.
490 1 $a Wireless networks, $x 2366-1445
505 0 $a Part 1 Fundamentals and Background -- 1 Introduction -- 2 Fundamentals of Federated Learning -- Part 2 Wireless Federated Learning: Design and Analysis 3 Resource Optimization for Wireless Federated Learning -- 4 Incentive Mechanisms for Federated Learning -- 5 Security and Privacy -- 6 Unsupervised Federated Learning -- Part 3 Federated Learning Applications in Wireless Networks -- 7 Wireless Virtual Reality -- 8 Vehicular Networks and Autonomous Driving Cars -- 9 Smart Industries and Intelligent Reflecting Surfaces.
520 $a 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 optimization theory, 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.
650 0 $a Machine learning. $3 533906
650 0 $a Wireless communication systems. $3 567106
650 1 4 $a Computer Communication Networks. $3 775497
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Cloud Computing. $3 3231328
700 1 $a Hong, Choong Seon. $3 3600957
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Wireless networks. $3 2162432
856 4 0 $u https://doi.org/10.1007/978-981-16-4963-9
950 $a Computer Science (SpringerNature-11645)