计算机科学
杠杆(统计)
分布式计算
架空(工程)
边缘计算
方案(数学)
GSM演进的增强数据速率
边缘设备
异步通信
人工智能
计算机网络
云计算
数学
操作系统
数学分析
作者
Yunlong Lu,Xiaotao Huang,Ke Zhang,Sabita Maharjan,Yan Zhang
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2021-08-01
卷期号:17 (8): 5709-5718
被引量:80
标识
DOI:10.1109/tii.2020.3010798
摘要
The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.
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