Distributed collaborative anomalous sound detection by embedding sharing

arXiv:2403.16610v1 Announce Type: cross
Abstract: To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. In this paper, we explore a method for multiple clients to collaboratively learn an anomalous sound detection model while keeping their raw data private from each other. In the context of industrial machine anomalous sound detection, each client possesses data from different machines or different operational states, making it challenging to learn through federated learning or split learning. In our …

arxiv clients context cs.cr cs.lg cs.sd data detection distributed eess.as industrial learn machine monitoring private sharing sound sound detection system