Active federated transfer algorithm based on broad learning for fault diagnosis. (28th February 2023)

• Record Type: Journal Article
• Authors: Liu, Guokai; Shen, Weiming; Gao, Liang; Kusiak, Andrew
• Is Part Of: Measurement. Volume 208(2023)
• Publication Date: 2023-02-28
• Subjects: Active learning -- Broad learning -- Cross-domain -- Fault diagnosis -- Federated learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2023.112452
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Federated learning (FL) guaranteeing data privacy is of great interest in decentralized fault diagnosis. However, limited research attention has been paid to the dynamic domain-shift issue due to varying working conditions. This paper proposes an active federated transfer algorithm based on broad learning to address the domain shift issue in FL. First, a central server dispatches a global model to the source clients for collaborative modeling. Subsequently, the global model is initialized with a federated averaging strategy. Next, the initialized global model is used to annotate emerging signals from the target clients based on an active sampling strategy proposed. Finally, an asynchronous update scheme is designed to adapt the global model to the target domain. The performance of the AFTBL algorithm is validated with three datasets, including 24 centralized- and decentralized-modeling tasks. The computational results indicate that the proposed algorithm is more accurate and efficient than the prevalent algorithms. Highlights: The cross-domain incremental federated learning problem is investigated. An active federated broad transfer algorithm is proposed for fault diagnosis. Automated data annotation and selection are proposed for incremental model update.