Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling

Xuanzhu Sheng, Chao Yu, Xiaolong Cui () and Yang Zhou
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Xuanzhu Sheng: Chinese People’s Armed Police Force Engineering University, Xi’an 710086, China
Chao Yu: Department of Electronic Technology, Wuhan Naval University of Engineering, Wuhan 430033, China
Xiaolong Cui: Chinese People’s Armed Police Force Engineering University, Xi’an 710086, China
Yang Zhou: School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China

Mathematics, 2024, vol. 12, issue 22, 1-21

Abstract: With the advancement of the large language model (LLM), the demand for data labeling services has increased dramatically. Big models are inseparable from high-quality, specialized scene data, from training to deploying application iterations to landing generation. However, how to achieve intelligent labeling consistency and accuracy and improve labeling efficiency in distributed data middleware scenarios is the main difficulty in enhancing the quality of labeled data at present. In this paper, we proposed an asynchronous federated learning optimization method based on the combination of LLM and digital twin technology. By analysising and comparing and with other existing asynchronous federated learning algorithms, the experimental results show that our proposed method outperforms other algorithms in terms of performance, such as model accuracy and running time. The experimental validation results show that our proposed method has good performance compared with other algorithms in the process of intelligent labeling both in terms of accuracy and running solves the consistency and accuracy problems of intelligent labeling in a distributed data center.

Keywords: large language model; digital twins; intelligent labeling; asynchronous federated learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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