語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Federated Learning for Wireless Networks
~
Han, Zhu.
Federated Learning for Wireless Networks
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Federated Learning for Wireless Networks/ by Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu Han.
作者:
Hong, Choong Seon.
其他作者:
Khan, Latif U.
面頁冊數:
XII, 253 p. 1 illus.online resource. :
Contained By:
Springer Nature eBook
標題:
Computer communication systems. -
電子資源:
https://doi.org/10.1007/978-981-16-4963-9
ISBN:
9789811649639
Federated Learning for Wireless Networks
Hong, Choong Seon.
Federated Learning for Wireless Networks
[electronic resource] /by Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu Han. - 1st ed. 2021. - XII, 253 p. 1 illus.online resource. - 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:
1115394
Computer communication systems.
LC Class. No.: TK5105.5-5105.9
Dewey Class. No.: 004.6
Federated Learning for Wireless Networks
LDR
:04116nam a22004095i 4500
001
1058029
003
DE-He213
005
20211202011836.0
007
cr nn 008mamaa
008
220103s2021 si | s |||| 0|eng d
020
$a
9789811649639
$9
978-981-16-4963-9
024
7
$a
10.1007/978-981-16-4963-9
$2
doi
035
$a
978-981-16-4963-9
050
4
$a
TK5105.5-5105.9
072
7
$a
UKN
$2
bicssc
072
7
$a
COM075000
$2
bisacsh
072
7
$a
UKN
$2
thema
082
0 4
$a
004.6
$2
23
100
1
$a
Hong, Choong Seon.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
793399
245
1 0
$a
Federated Learning for Wireless Networks
$h
[electronic resource] /
$c
by Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu Han.
250
$a
1st ed. 2021.
264
1
$a
Singapore :
$b
Springer Singapore :
$b
Imprint: Springer,
$c
2021.
300
$a
XII, 253 p. 1 illus.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
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
Computer communication systems.
$3
1115394
650
0
$a
Machine learning.
$3
561253
650
0
$a
Computer software.
$3
528062
650
1 4
$a
Computer Communication Networks.
$3
669310
650
2 4
$a
Machine Learning.
$3
1137723
650
2 4
$a
Professional Computing.
$3
1115983
700
1
$a
Khan, Latif U.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1305402
700
1
$a
Chen, Mingzhe.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1363564
700
1
$a
Chen, Dawei.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1363565
700
1
$a
Saad, Walid.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1141494
700
1
$a
Han, Zhu.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1019368
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9789811649622
776
0 8
$i
Printed edition:
$z
9789811649646
776
0 8
$i
Printed edition:
$z
9789811649653
830
0
$a
Wireless Networks,
$x
2366-1186
$3
1258208
856
4 0
$u
https://doi.org/10.1007/978-981-16-4963-9
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入