Heterogeneous Federated Learning: Client-Side Collaborative Update Interdomain Generalization Method for Intelligent Fault Diagnosis

The federated fault diagnosis approach has achieved remarkable results in recent years, which enables multiple clients with similar mechanical devices to collaboratively construct global intelligent diagnostic models while protecting data privacy. However, in practice, the statistical heterogeneity of data collected from different clients, as well as the model heterogeneity due to local model personalization, pose great challenges to federated learning (FL). Meanwhile, using a central server as an information management center to build global models increases additional model parameters and the risk of data privacy leakage. To address these issues, this article proposes a heterogeneous FL framework based on peer-to-peer communication (P2PCHF) for rotating machinery fault diagnosis. To achieve heterogeneous client communication without relying on a central server, the sharing unlabeled dataset is utilized in the collaborative updating phase to achieve peer-to-peer communication between clients and to align instance dimensions and clustering dimensions between heterogeneous clients by constructing intercorrelation matrices to achieve feature-level and semantic-level knowledge exchange for better interdomain generalization capabilities. Joint knowledge distillation based on class labels and class relations is introduced in the local update phase to mitigate forgetting effect in the local update phase of private models and effectively balance multidomain category knowledge. It is verified in three cases that the proposed P2PCHF can effectively address model heterogeneity and data statistics heterogeneity among clients, and enable locally-privatized models to gain interdomain generalization capability. The code framework is available at https://github.com/JC952/P2PCHF.