Guest Editorial: Special Issue on Resource-Efficient Collaborative Deep Learning Over B5G/6G Networks

被引:0
作者
Brik, Bouziane [1 ]
Bennis, Mehdi [2 ]
Wang, Xianbin [3 ]
Guizani, Mohsen [4 ]
机构
[1] Sharjah Univ, Coll Comp & Informat, Comp Sci Dept, Sharjah, U Arab Emirates
[2] Univ Oulu, Fac Informat Technol & Elect Engn, Ctr Wireless Commun, Oulu 90570, Finland
[3] Western Univ, Innovat Ctr Informat Engn, London N6A 3K7, ON, Canada
[4] Mohamed Bin Zayed Univ Artificial Intelligence MBZ, Machine Learning Dept, Abu Dhabi, U Arab Emirates
来源
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY | 2024年 / 5卷
关键词
Special issues and sections; 6G mobile communication; Federated learning; Machine learning; Intelligent networks; Deep learning;
D O I
10.1109/OJCOMS.2023.3348029
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Collaborative machine learning is considered as the bedrock of the intelligent B5G networks, where distributed agents collaborate with each other to train learning models in a distributed fashion, without sharing data at a central entity. Despite its broad applicability, the main issue of collaborative learning is the need of local computing to build local learning models as well as iterative information exchange among agents, which may lead to high resource overhead unaffordable in many practical resource-limited systems such as unmanned aerial vehicles (UAVs) and Internet of Things (IoT). To alleviate this resource issue, it is essential to devise resource-efficient collaborative learning techniques, that can optimize the resource overhead in terms of communication, computing, and energy cost, and hence achieve satisfactory optimization/learning performance simultaneously.
引用
收藏
页码:1026 / 1028
页数:3
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