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221004 ||| eng |
| 020 |
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|a 9783031078385
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| 100 |
1 |
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|a Lim, Wei Yang Bryan
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| 245 |
0 |
0 |
|a Federated Learning Over Wireless Edge Networks
|h Elektronische Ressource
|c by Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao
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| 250 |
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|a 1st ed. 2022
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2022, 2022
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| 300 |
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|a XV, 165 p. 51 illus., 47 illus. in color
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Machine learning
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| 653 |
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|a Machine Learning
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Telecommunication
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Communications Engineering, Networks
|
| 700 |
1 |
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|a Ng, Jer Shyuan
|e [author]
|
| 700 |
1 |
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|a Xiong, Zehui
|e [author]
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| 700 |
1 |
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|a Niyato, Dusit
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
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|a Wireless Networks
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| 028 |
5 |
0 |
|a 10.1007/978-3-031-07838-5
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-031-07838-5?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 621.382
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| 520 |
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|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
|