Federated Learning for Wireless Networks (Record no. 178774)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05009nam a22006015i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-981-16-4963-9 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240423125520.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 211202s2021 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789811649639 |
| -- | 978-981-16-4963-9 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-981-16-4963-9 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TK5105.5-5105.9 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UKN |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM043000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UKN |
| Source | thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 004.6 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Hong, Choong Seon. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 245 10 - TITLE STATEMENT | |
| Title | Federated Learning for Wireless Networks |
| Medium | [electronic resource] / |
| Statement of responsibility, etc | by Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu Han. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2021. |
| 264 #1 - | |
| -- | Singapore : |
| -- | Springer Nature Singapore : |
| -- | Imprint: Springer, |
| -- | 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XII, 253 p. 1 illus. |
| Other physical details | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 490 1# - SERIES STATEMENT | |
| Series statement | Wireless Networks, |
| International Standard Serial Number | 2366-1445 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer networks . |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Cloud Computing. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer Communication Networks. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Cloud Computing. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Khan, Latif U. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chen, Mingzhe. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chen, Dawei. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Saad, Walid. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Han, Zhu. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9789811649622 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9789811649646 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9789811649653 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Wireless Networks, |
| -- | 2366-1445 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-981-16-4963-9">https://doi.org/10.1007/978-981-16-4963-9</a> |
| 912 ## - | |
| -- | ZDB-2-SCS |
| 912 ## - | |
| -- | ZDB-2-SXCS |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks-CSE-Springer |
No items available.