作者
Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan, Yan Zhang
发表日期
2020/8/18
期刊
IEEE Transactions on Industrial Informatics
卷号
17
期号
7
页码范围
5098-5107
出版商
IEEE
简介
Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework …
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