FedAlign: Federated Model Alignment via Data-Free Knowledge Distillation for Machine Fault Diagnosis

Due to privacy issues, the data island problem of machine fault diagnosis widely exists in real industry. Federated learning (FL) has received much attention as a decentralized machine-learning paradigm that learns a global model in the server by iteratively aggregating the local model parameters in a privacy-preserving scheme to address the data island problem. However, the fault data in industry is inevitably scarce and class imbalanced, so the fault data in different clients are heterogeneous from each other in FL. The existing federated machine fault diagnosis methods are mostly based on the federated averaging (FedAvg) algorithm, which ignores the data heterogeneity issue for machine fault diagnosis. To address this issue for machine fault diagnosis, we propose a new model alignment method (called FedAlign) via data-free knowledge distillation, where a compact generator is trained in the server in a data-free manner to estimate the data feature distribution in a global view without access to the local data. Then, the generator can produce the pseudo features to convey the distribution knowledge to both the server and the client sides. Our proposed FedAlign utilizes the pseudo features to align the global model and the local models to finally address the inherent heterogeneity of local data for fault diagnosis in FL. Experiments performed on fault diagnosis datasets of nonidentically and independently (non-iid) settings indicate that our proposed method facilitates FL for machine fault diagnosis with favorable effectiveness and achieves significant performance gains compared with state-of-the-art methods.