Communication-Efficient Federated Learning for Digital Twin Systems of Industrial Internet of Things

With the rapid development and deployment of Industrial Internet of Things technology, it promotes interconnection and edge applications in smart manufacturing. However, challenges remain, such as yet-to-improve communication efficiency and trade-offs between computing power and energy consumption, which limits the application and further development of IIoT technology. This paper proposes the digital twin systems into the IIoT to build model between physical objects and digital virtual systems to optimize the structure of IIoT. And we further introduce federal learning to train the digital twins model and to improve the communication efficiency of IIoT. In this paper, we first establish the digital twins model of IIoT based on industrial scenario. Moreover, to optimize the communication overhead allocation problem, this paper proposes an improved communication-efficient distribution algorithm, which speeds up the training performance of federated model and ensures the performance of industrial system model by changing the update training mode of client and server and allowing some industrial equipment to participate in federated training. This paper simulates the real-word intelligent camera detection to validate the proposed method. Comparing our proposed method with the existing traditional methods, the results show the advantages of the proposed method can improve the communication performance of the training model. Copyright (c) 2022 The Authors .This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Automated summary

This paper introduces the application of digital twin systems and federated learning in Industrial Internet of Things to optimize the structure and improve communication efficiency.