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Federated learning over wireless edg...

Lim, Wei Yang Bryan.

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Federated learning over wireless edge networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Federated learning over wireless edge networks/ by Wei Yang Bryan Lim ... [et al.].
other author:	Lim, Wei Yang Bryan.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xv, 165 p. :ill., digital ;24 cm.
[NT 15003449]:	Federated Learning at Mobile Edge Networks: A Tutorial -- Multi-Dimensional Contract Matching Design for Federated Learning in UAV Networks -- Joint Auction-Coalition Formation Framework for UAV-assisted Communication-Efficient Federated Learning -- Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- Conclusion and Future Works.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-07838-5
ISBN:	9783031078385

Federated learning over wireless edge networks
Federated learning over wireless edge networks[electronic resource] /by Wei Yang Bryan Lim ... [et al.]. - Cham :Springer International Publishing :2022. - xv, 165 p. :ill., digital ;24 cm. - Wireless networks,2366-1445. - Wireless networks..

Federated Learning at Mobile Edge Networks: A Tutorial -- Multi-Dimensional Contract Matching Design for Federated Learning in UAV Networks -- Joint Auction-Coalition Formation Framework for UAV-assisted Communication-Efficient Federated Learning -- Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- Conclusion and Future Works.

This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively. Provides a concise introduction to Federated Learning (FL) and how it enables Edge Intelligence; Highlights the challenges inherent to achieving scalable implementation of FL at the wireless edge; Presents how FL can address challenges resulting from the confluence of AI and wireless communications.

ISBN: 9783031078385

Standard No.: 10.1007/978-3-031-07838-5doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Federated learning over wireless edge networks
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245 0 0 $a Federated learning over wireless edge networks $h [electronic resource] / $c by Wei Yang Bryan Lim ... [et al.].
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300 $a xv, 165 p. : $b ill., digital ; $c 24 cm.
490 1 $a Wireless networks, $x 2366-1445
505 0 $a Federated Learning at Mobile Edge Networks: A Tutorial -- Multi-Dimensional Contract Matching Design for Federated Learning in UAV Networks -- Joint Auction-Coalition Formation Framework for UAV-assisted Communication-Efficient Federated Learning -- Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- Conclusion and Future Works.
520 $a This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively. Provides a concise introduction to Federated Learning (FL) and how it enables Edge Intelligence; Highlights the challenges inherent to achieving scalable implementation of FL at the wireless edge; Presents how FL can address challenges resulting from the confluence of AI and wireless communications.
650 0 $a Machine learning. $3 533906
650 0 $a Wireless communication systems. $3 567106
650 0 $a Edge computing. $3 3489844
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Lim, Wei Yang Bryan. $3 3605586
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/10.1007/978-3-031-07838-5
950 $a Computer Science (SpringerNature-11645)