Federated Domain Generalization for Fault Diagnosis: Cross-Client Style Integration and Dual Alignment Representation

Abstract

Federated learning (FL) enables the collaborative training of intelligent diagnostic models across multiple clients while preserving data privacy. However, the complex and dynamic working conditions often cause federated diagnostic models to suffer from performance degradation when applied to unseen clients, exposing deficiencies in model generalization. Domain generalization fault diagnosis methods aim to enhance model adaptability under unknown working conditions, but most existing algorithms rely on shared data, making them unsuitable for direct application in FL environments. To address this challenge, we propose a federated domain generalization fault diagnosis method based on Cross-Client Style Integration and Dual Alignment representation (CCSIDA). This method significantly enhances model generalization in FL settings while ensuring data privacy. Specifically, CCSIDA integrates three key strategies: 1) cross-client style data enhancement; 2) domain-sensitive feature suppression (DSFS); and 3) predictive alignment. For cross-client style integration, we design a stochastic mixed-style sample generation module to overcome the limitations of conventional domain generalization data enhancement methods in FL. This module generates diverse samples by constructing a shared style repository, enabling interclient style information transfer. For cross-client dual alignment representation, we introduce a DSFS strategy to reduce domain discrepancies between original and enhanced samples. Additionally, Jensen-Shannon divergence is employed to ensure prediction consistency between these samples. Finally, extensive experiments on five benchmark datasets validate the effectiveness of the proposed method.