Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT

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.

Automated summary

This article introduces the application of digital twin edge networks in the industrial Internet of Things, filling the gap between physical systems and digital spaces by incorporating digital twin into edge networks. The use of federated learning to construct digital twin models of IoT devices and the proposal of an asynchronous model update scheme help to improve communication efficiency and reduce transmission energy cost.