Name
Title | ||
|---|---|---|
Accelerated Unsupervised Clustering in Acoustic Sensor Networks Using Federated Learning and a Variational Autoencoder |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/IWAENC53105.2022.9914753 | 2022 International Workshop on Acoustic Signal Enhancement (IWAENC) |
Keywords | DocType | ISBN |
clustered federated learning,DNN,autoencoder,acoustic sensor networks,distributed classification | Conference | 978-1-6654-6868-8 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Becker | 1 | 0 | 0.34 |
| Alexandru Nelus | 2 | 0 | 0.34 |
| Rene Glitza | 3 | 0 | 0.34 |
| Rainer Martin | 4 | 1029 | 91.14 |