A personalized federated meta-learning method for intelligent and privacy-preserving fault diagnosis

Abstract

Intelligent fault diagnosis methods have made significant progress during the past decade, often following a centralized training paradigm that gathers data from multiple industrial users, which results in high acquisition costs and raise data privacy concerns. Federated Learning (FL) provides a promising solution to intrinsically protect data privacy by jointly training a shared global model without sharing individual data, but the varying data distributions among clients pose challenges for diagnostic performance. To address these problems, a novel FL framework called federated meta-learning based on fine-grained classifier reconstruction (FedFGCR) is presented in this paper. First, an adaptive interpolation strategy based on meta-learning is designed to compute the optimal combination of global and local feature extractors as the initialization of the local training. Additionally, we introduce a new regularization term that allows the local model to leverage global data, enhancing model generalization by incorporating global semantic feature information. Furthermore, a fine-grained classifier reconstruction method is proposed to improve the personalized diagnosis ability for local samples. Comparative experiments with mainstream FL-based fault diagnosis methods have been conducted, demonstrating FedFGCR’s superiority with accuracy gains ranging from 0.96% to 5.41% on the CWRU dataset and from 5.01% to 9.95% on the KAIST dataset.