Margin-guided parameter decoupling-consensus framework for federated domain generalization in machinery fault diagnosis

Federated domain generalization (FDG) as a solution to address the cross-client data heterogeneity problem in privacy-sensitive scenarios has drawn extensive attention in the field of intelligent fault diagnosis of industrial equipment in recent years. Nevertheless, most of the existing FDG-based diagnosis methods rely on client feature distribution alignment or data augmentation strategies, risking data leakage caused by the transmission of deep features and statistical information. To overcome the above-mentioned issues, a margin-guided parameter decoupling-consensus (MGPDC) framework is proposed to decouple the dependence of conventional federated domain generalization methods on features and data distributions and realize the extraction of common knowledge across clients. This framework initially employs a federated meta-learning-driven universal feature extractor to create a transferable shared feature space amidst heterogeneous client data, effectively enhancing the generalization ability of the model for unknown working conditions. Next, a parameter decoupling-consensus synergy (PDCS) mechanism is proposed. In this mechanism, an isolation module is established based on the consistency of parameter updates for parameter decoupling, effectively suppressing model update conflict. Subsequently, an implicit alignment mapping approach is devised for the screened parameters with strong consistency to achieve the extraction of cross-domain common knowledge. Then, an adaptive global margin guidance (AGMG) strategy is proposed to mitigate the interference of the blurred class boundaries during the federated process on common knowledge extraction. Finally, extensive experiments using real wind turbine gearbox data demonstrate the effectiveness and advancement of the MGPDC framework.