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Communication Efficient Federated Learning for Wireless Networks (Record no. 57707)

MARC details
000 -LEADER
fixed length control field 05347nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-031-51266-7
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251030111431.0
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 240219s2024 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031512667
-- 978-3-031-51266-7
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-031-51266-7
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
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Chen, Mingzhe.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
9 (RLIN) 71263
245 10 - TITLE STATEMENT
Title Communication Efficient Federated Learning for Wireless Networks
Medium [electronic resource] /
Statement of responsibility, etc. by Mingzhe Chen, Shuguang Cui.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2024.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cham :
Name of producer, publisher, distributor, manufacturer Springer Nature Switzerland :
-- Imprint: Springer,
Date of production, publication, distribution, manufacture, or copyright notice 2024.
300 ## - PHYSICAL DESCRIPTION
Extent XI, 179 p. 46 illus., 44 illus. in color.
Other physical details online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
490 1# - SERIES STATEMENT
Series statement Wireless Networks,
International Standard Serial Number 2366-1445
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Part. I. Fundamentals and Preliminaries of Federated Learning -- Chapter. 1. Introduction -- Chapter. 2. Fundamentals and Preliminaries of Federated Learning -- Chapter. 3. Resource Management for Federated Learning -- Chapter. 4. Quantization for Federated Learning -- Chapter. 5. Federated Learning with Over the Air Computation -- Chapter. 6. Federated Learning for Autonomous Vehicles Control -- Chapter. 7. Federated Learning for Mobile Edge Computing.
520 ## - SUMMARY, ETC.
Summary, etc. This book provides a comprehensive study of Federated Learning (FL) over wireless networks. It consists of three main parts: (a) Fundamentals and preliminaries of FL, (b) analysis and optimization of FL over wireless networks, and (c) applications of wireless FL for Internet-of-Things systems. In particular, in the first part, the authors provide a detailed overview on widely-studied FL framework. In the second part of this book, the authors comprehensively discuss three key wireless techniques including wireless resource management, quantization, and over-the-air computation to support the deployment of FL over realistic wireless networks. It also presents several solutions based on optimization theory, graph theory and machine learning to optimize the performance of FL over wireless networks. In the third part of this book, the authors introduce the use of wireless FL algorithms for autonomous vehicle control and mobile edge computing optimization. Machine learning and data-driven approaches have recently received considerable attention as key enablers for next-generation intelligent networks. Currently, most existing learning solutions for wireless networks rely on centralizing the training and inference processes by uploading data generated at edge devices to data centers. However, such a centralized paradigm may lead to privacy leakage, violate the latency constraints of mobile applications, or may be infeasible due to limited bandwidth or power constraints of edge devices. To address these issues, distributing machine learning at the network edge provides a promising solution, where edge devices collaboratively train a shared model using real-time generated mobile data. The avoidance of data uploading to a central server not only helps preserve privacy but also reduces network traffic congestion as well as communication cost. Federated learning (FL) is one of most important distributed learning algorithms. In particular, FL enables devices to train a shared machine learning model while keeping data locally. However, in FL, training machine learning models requires communication between wireless devices and edge servers over wireless links. Therefore, wireless impairments such as noise, interference, and uncertainties among wireless channel states will significantly affect the training process and performance of FL. For example, transmission delay can significantly impact the convergence time of FL algorithms. In consequence, it is necessary to optimize wireless network performance for the implementation of FL algorithms. This book targets researchers and advanced level students in computer science and electrical engineering. Professionals working in signal processing and machine learning will also buy this book.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 60157
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 55059
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 65781
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 65782
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 65689
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 55059
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 65786
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Cui, Shuguang.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
9 (RLIN) 71264
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
Relationship information Printed edition:
International Standard Book Number 9783031512650
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031512674
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031512681
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Wireless Networks,
International Standard Serial Number 2366-1445
9 (RLIN) 67497
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-031-51266-7">https://doi.org/10.1007/978-3-031-51266-7</a>
912 ## -
-- ZDB-2-SCS
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-- ZDB-2-SXCS

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