Kavli Affiliate: Jia Liu
| First 5 Authors: Anbai Jiang, Yuchen Shi, Pingyi Fan, Wei-Qiang Zhang, Jia Liu
| Summary:
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.
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