Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/12/22/3550 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850267057867718656 |
|---|---|
| author | Xuanzhu Sheng Chao Yu Xiaolong Cui Yang Zhou |
| author_facet | Xuanzhu Sheng Chao Yu Xiaolong Cui Yang Zhou |
| author_sort | Xuanzhu Sheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6fb1f4e9954f4373a84891e5e581ee73 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-6fb1f4e9954f4373a84891e5e581ee732025-08-20T01:53:57ZengMDPI AGMathematics2227-73902024-11-011222355010.3390/math12223550Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent LabelingXuanzhu Sheng0Chao Yu1Xiaolong Cui2Yang Zhou3Chinese People’s Armed Police Force Engineering University, Xi’an 710086, ChinaDepartment of Electronic Technology, Wuhan Naval University of Engineering, Wuhan 430033, ChinaChinese People’s Armed Police Force Engineering University, Xi’an 710086, ChinaSchool of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, ChinaWith 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.https://www.mdpi.com/2227-7390/12/22/3550large language modeldigital twinsintelligent labelingasynchronous federated learning |
| spellingShingle | Xuanzhu Sheng Chao Yu Xiaolong Cui Yang Zhou Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling Mathematics large language model digital twins intelligent labeling asynchronous federated learning |
| title | Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling |
| title_full | Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling |
| title_fullStr | Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling |
| title_full_unstemmed | Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling |
| title_short | Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling |
| title_sort | large language model and digital twins empowered asynchronous federated learning for secure data sharing in intelligent labeling |
| topic | large language model digital twins intelligent labeling asynchronous federated learning |
| url | https://www.mdpi.com/2227-7390/12/22/3550 |
| work_keys_str_mv | AT xuanzhusheng largelanguagemodelanddigitaltwinsempoweredasynchronousfederatedlearningforsecuredatasharinginintelligentlabeling AT chaoyu largelanguagemodelanddigitaltwinsempoweredasynchronousfederatedlearningforsecuredatasharinginintelligentlabeling AT xiaolongcui largelanguagemodelanddigitaltwinsempoweredasynchronousfederatedlearningforsecuredatasharinginintelligentlabeling AT yangzhou largelanguagemodelanddigitaltwinsempoweredasynchronousfederatedlearningforsecuredatasharinginintelligentlabeling |