Federated Learning Over Wireless Edge Networks /
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...
| Main Authors: | , , , , |
|---|---|
| Format: | text |
| Language: | eng |
| Published: |
Cham, Switzerland : Springer,
2022
|
| Subjects: |
| _version_ | 1811129099968577536 |
|---|---|
| author | Lim, Wei Yang Bryan 654011 Ng, Jer Shyuan, author 654012 Xiong, Zehui, author 654013 Niyato, Dusit, author 564796 Miao, Chunyan, author 654014 |
| author_facet | Lim, Wei Yang Bryan 654011 Ng, Jer Shyuan, author 654012 Xiong, Zehui, author 654013 Niyato, Dusit, author 564796 Miao, Chunyan, author 654014 |
| author_sort | Lim, Wei Yang Bryan 654011 |
| collection | OCEAN |
| description | 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. |
| first_indexed | 2024-07-04T04:50:55Z |
| format | text |
| id | KOHA-OAI-TEST:611230 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-09-23T23:42:50Z |
| publishDate | 2022 |
| publisher | Cham, Switzerland : Springer, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:6112302024-09-07T12:11:01ZFederated Learning Over Wireless Edge Networks / Lim, Wei Yang Bryan 654011 Ng, Jer Shyuan, author 654012 Xiong, Zehui, author 654013 Niyato, Dusit, author 564796 Miao, Chunyan, author 654014 textCham, Switzerland : Springer,2022©2022engThis 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.Includes bibliographical references and index.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.Springer Nature;Wireless communication systemsMachine learningEdge computingURN:ISBN:9783031078378 |
| spellingShingle | Wireless communication systems Machine learning Edge computing Lim, Wei Yang Bryan 654011 Ng, Jer Shyuan, author 654012 Xiong, Zehui, author 654013 Niyato, Dusit, author 564796 Miao, Chunyan, author 654014 Federated Learning Over Wireless Edge Networks / |
| title | Federated Learning Over Wireless Edge Networks / |
| title_full | Federated Learning Over Wireless Edge Networks / |
| title_fullStr | Federated Learning Over Wireless Edge Networks / |
| title_full_unstemmed | Federated Learning Over Wireless Edge Networks / |
| title_short | Federated Learning Over Wireless Edge Networks / |
| title_sort | federated learning over wireless edge networks |
| topic | Wireless communication systems Machine learning Edge computing |
| work_keys_str_mv | AT limweiyangbryan654011 federatedlearningoverwirelessedgenetworks AT ngjershyuanauthor654012 federatedlearningoverwirelessedgenetworks AT xiongzehuiauthor654013 federatedlearningoverwirelessedgenetworks AT niyatodusitauthor564796 federatedlearningoverwirelessedgenetworks AT miaochunyanauthor654014 federatedlearningoverwirelessedgenetworks |