Decentralized federated domain generalization with cluster alignment for fault diagnosis

Abstract

Fault diagnosis is important for maintaining safety in industrial scenarios. Due to the complex operating conditions, there is usually a domain shift between training (source) data and testing (target) data. Recent years have witnessed the emergence of numerous transfer learning methods dealing with the domain shift. However, existing methods often fail to deal with the situation where target data are unavailable. Moreover, the majority of these methods require aggregating data distributed across various users (nodes) for model training, raising privacy concerns. Despite existing federated learning methods can protect privacy, they mostly rely on a central server, which may lead to a single point of failure. To address the above issues, we propose a fully decentralized Federated Domain Generalization with Cluster Alignment (FDG-CA), which deals with the domain shift problem without accessing target data and eliminates the need of a central server while protecting privacy. During the training phase, the proposed FDG-CA learns domain-invariant representations by aligning clusters statistics of different source nodes through information exchange. Subsequently, during the testing phase, we propose an ensemble strategy based on a learner filter and a voting scheme to get the prediction results. Experiments demonstrate that our proposed method is superior to existing methods, achieving higher accuracy while addressing privacy concerns.