FedGNN-SFD: A Lightweight Federated Graph Neural Network for Multi-Sensor Bearing Fault Diagnosis in Industrial IoT Systems

Bearing fault diagnosis in industrial Internet of Things (IIoT) systems faces critical challenges: the need for lightweight models deployable on edge devices, privacy constraints preventing centralized data aggregation, and complex inter-sensor correlations in multi-sensor monitoring systems. This paper proposes FedGNN-SFD, a Federated Graph Neural Network that addresses these challenges through a lightweight graph attention architecture. On the CWRU bearing fault dataset with 1,658 samples across 10 fault categories, FedGNN-SFD achieves 87.95% accuracy with only 69,706 parameters—62 times smaller than CNN-1D (4.34M). Comprehensive experiments demonstrate: (1) the graph attention module contributes +12.5% accuracy improvement compared to simple pooling (91.77% vs 79.32%); (2) federated learning with 5 clients and Non-IID data achieves 87.35% accuracy within 15 communication rounds with only 0.6% gap from centralized training; (3) noise robustness analysis shows stable performance under moderate noise conditions; (4) cross-domain validation across simulated load variations demonstrates consistent generalization. The results validate the effectiveness of the proposed architecture for edge deployment scenarios where model efficiency and privacy preservation are prioritized.