Guest Editorial Special Section on Distributed Edge Learning in Wireless Networks
Distributed machine learning at the network edge has emerged as a promising new paradigm. Various machine learning (ML) technologies will distill Artificial Intelligence (AI) from enormous mobile data to automate future wireless networking and a wide range of Internet-of-Things (IoT) applications. I...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Online Access: | https://ieeexplore.ieee.org/document/10306302/ |
| _version_ | 1797637711115321344 |
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| author | Xiaowen Gong Kaibin Huang Mingzhe Chen Carlo Fischione Jun Zhang Wan Choi |
| author_facet | Xiaowen Gong Kaibin Huang Mingzhe Chen Carlo Fischione Jun Zhang Wan Choi |
| author_sort | Xiaowen Gong |
| collection | DOAJ |
| description | Distributed machine learning at the network edge has emerged as a promising new paradigm. Various machine learning (ML) technologies will distill Artificial Intelligence (AI) from enormous mobile data to automate future wireless networking and a wide range of Internet-of-Things (IoT) applications. In distributed edge learning, multiple edge devices train a common learning model collaboratively without sending their raw data to a central server, which not only helps to preserve data privacy but also reduces network traffic. However, distributed edge training and edge inference typically still require extensive communications among devices and servers connected by wireless links. As a result, the salient features of wireless networks, including interference and channels’ heterogeneity, time-variability, and unreliability, have significant impacts on the learning performance. |
| first_indexed | 2024-03-11T12:53:17Z |
| format | Article |
| id | doaj.art-1d4363a9462c4e20a863e8a4cf5539c8 |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-03-11T12:53:17Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-1d4363a9462c4e20a863e8a4cf5539c82023-11-03T23:01:05ZengIEEEIEEE Open Journal of the Communications Society2644-125X2023-01-0142729273210.1109/OJCOMS.2023.332779910306302Guest Editorial Special Section on Distributed Edge Learning in Wireless NetworksXiaowen Gong0https://orcid.org/0000-0001-5124-7941Kaibin Huang1Mingzhe Chen2Carlo Fischione3Jun Zhang4Wan Choi5Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USADepartment of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong KongDivision of Networks and System Engineering, KTH Royal Institute of Technology, Stockholm, SwedenDepartment of Electrical and Computer Engineering, University of Miami, Miami, FL, USADepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Sai Kung, Hong KongDepartment of Electrical and Computer Engineering, Seoul National University, Seoul, South KoreaDistributed machine learning at the network edge has emerged as a promising new paradigm. Various machine learning (ML) technologies will distill Artificial Intelligence (AI) from enormous mobile data to automate future wireless networking and a wide range of Internet-of-Things (IoT) applications. In distributed edge learning, multiple edge devices train a common learning model collaboratively without sending their raw data to a central server, which not only helps to preserve data privacy but also reduces network traffic. However, distributed edge training and edge inference typically still require extensive communications among devices and servers connected by wireless links. As a result, the salient features of wireless networks, including interference and channels’ heterogeneity, time-variability, and unreliability, have significant impacts on the learning performance.https://ieeexplore.ieee.org/document/10306302/ |
| spellingShingle | Xiaowen Gong Kaibin Huang Mingzhe Chen Carlo Fischione Jun Zhang Wan Choi Guest Editorial Special Section on Distributed Edge Learning in Wireless Networks IEEE Open Journal of the Communications Society |
| title | Guest Editorial Special Section on Distributed Edge Learning in Wireless Networks |
| title_full | Guest Editorial Special Section on Distributed Edge Learning in Wireless Networks |
| title_fullStr | Guest Editorial Special Section on Distributed Edge Learning in Wireless Networks |
| title_full_unstemmed | Guest Editorial Special Section on Distributed Edge Learning in Wireless Networks |
| title_short | Guest Editorial Special Section on Distributed Edge Learning in Wireless Networks |
| title_sort | guest editorial special section on distributed edge learning in wireless networks |
| url | https://ieeexplore.ieee.org/document/10306302/ |
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