CoopASD: Cooperative Machine Anomalous Sound Detection with Privacy Concerns
This addresses privacy-preserving anomaly detection for factories in the Industrial Internet of Things, but it is incremental as it adapts existing methods to a decentralized setting.
The paper tackles the problem of machine anomalous sound detection in decentralized industrial settings where data cannot be shared due to privacy concerns, proposing CoopASD, a cooperative framework that achieves competitive results with only a 0.08% degradation compared to centralized state-of-the-art models.
Machine anomalous sound detection (ASD) has emerged as one of the most promising applications in the Industrial Internet of Things (IIoT) due to its unprecedented efficacy in mitigating risks of malfunctions and promoting production efficiency. Previous works mainly investigated the machine ASD task under centralized settings. However, developing the ASD system under decentralized settings is crucial in practice, since the machine data are dispersed in various factories and the data should not be explicitly shared due to privacy concerns. To enable these factories to cooperatively develop a scalable ASD model while preserving their privacy, we propose a novel framework named CoopASD, where each factory trains an ASD model on its local dataset, and a central server aggregates these local models periodically. We employ a pre-trained model as the backbone of the ASD model to improve its robustness and develop specialized techniques to stabilize the model under a completely non-iid and domain shift setting. Compared with previous state-of-the-art (SOTA) models trained in centralized settings, CoopASD showcases competitive results with negligible degradation of 0.08%. We also conduct extensive ablation studies to demonstrate the effectiveness of CoopASD.