Accelerated Unsupervised Clustering in Acoustic Sensor Networks Using Federated Learning and a Variational Autoencoder

In this paper we present an accelerated algorithm for clustering source-dominated microphones in acoustic sensor networks. Predicated on privacy-preserving unsupervised clustered federated learning that groups microphones by evaluating the similarity of model weight updates, we introduce a light-weight variational autoencoder and equip the algorithm with supplementary control criteria for faster convergence. We validate the quality, degree of acceleration and utility of our method using clustering-based and classification-based tasks. Compared to the previously employed deterministic autoencoder, we observe a significantly lower number of client-server communication rounds at the price of a minor reduction in clustering performance.