Personalised federated learning framework for damage detection in structural health monitoring

Abstract Data-driven machine learning models, compared to numerical models, demonstrated promising improvements in detecting damage in structural health monitoring (SHM) applications. In such approaches, sensors’ data are used to train a model either in a centralized model (server) or locally inside each sensor unit node (client). The centralized learning model often leads to computing and privacy issues such as wireless transmission costs and data-sensitive vulnerability, especially in real-time settings. The decentralized model also poses different challenges such as feature correlations and relationships loss in decentralized learning settings. To handle the shortcomings of both models, we propose a new Personalized federated learning (FL) model augmented with tensor data fusion to learn and detect damage in SHM. Our approach employs FL which enables the central machine learning model to gain experience from diverse datasets located at different sensor locations. Furthermore, our proposed model addresses the problems associated non-i.i.d. data by employing the Moreau envelopes as a regularized loss function in the learning process of client’s models. Our methods help in decoupling the client models from the central one which improves personalized in FL. Our experimental evaluation on real structural datasets demonstrates promising damage detection accuracy without the need to transmit the actual data to the centralized learning model. The results also show that the data correlations and relationships from all participating sensors are preserved.