DFed-LT: A Decentralized Federated Learning with Lightweight Transformer Network for Intelligent Fault Diagnosis

In recent years, deep learning has been increasingly applied in the field of fault diagnosis, but it currently faces two challenges: (1) data privacy issues prevent the aggregation of data from different users to form a large training dataset; (2) the limited memory of edge devices or handheld detection devices restricts the application of some larger structural models. To address these issues, this article proposes a lightweight federated learning method with transformer network for intelligent fault diagnosis. A federated learning architecture is constructed to achieve distributed learning of different user data, which not only ensures the privacy and security of user data, but also enables feature learning of different user data. In addition, the lightweight transformer network is built locally for different users to achieve the applicability of the model on different devices. An experimental case was implemented to demonstrate the effectiveness of the proposed method, and the results showed that the proposed method can achieve effective fault diagnosis while preserving data privacy. Compared with other methods, the proposed diagnostic model requires less computing resources. In addition, even under noisy conditions, the method maintains significant robustness against acoustic interference.