Graph Neural Network Based Asynchronous Federated Learning for Digital Twin-Driven Distributed Multi-Agent Dynamical Systems

Xuanzhu Sheng, Yang Zhou and Xiaolong Cui ()
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Xuanzhu Sheng: 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
Xiaolong Cui: Chinese People’s Armed Police Force Engineering University, Xi’an 710086, China

Mathematics, 2024, vol. 12, issue 16, 1-24

Abstract: The rapid development of artificial intelligence (AI) and 5G paradigm brings infinite possibilities for data annotation for new applications in the industrial Internet of Things (IIoT). However, the problem of data annotation consistency under distributed architectures and growing concerns about issues such as data privacy and cybersecurity are major obstacles to improving the quality of distributed data annotation. In this paper, we propose a reputation-based asynchronous federated learning approach for digital twins. First, this paper integrates digital twins into an asynchronous federated learning framework, and utilizes a smart contract-based reputation mechanism to enhance the interconnection and internal interaction of asynchronous mobile terminals. In addition, in order to enhance security and privacy protection in the distributed smart annotation system, this paper introduces blockchain technology to optimize the data exchange, storage, and sharing process to improve system security and reliability. The data results show that the consistency of our proposed FedDTrep distributed intelligent labeling system reaches 99%.

Keywords: digital twins; intelligent labeling; federated learning; federated trust mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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