# Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments

arXiv: https://arxiv.org/abs/2106.03671 · HTML: https://mlsanity.com/paper/2106.03671/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication](https://mlsanity.com/paper/1805.08768/) (2018)
- [Distributed Multi-Speaker Voice Activity Detection for Wireless Acoustic Sensor Networks](https://mlsanity.com/paper/1703.05782/) (2017)
- [Misalignment Recognition in Acoustic Sensor Networks using a Semi-supervised Source Estimation Method and Markov Random Fields](https://mlsanity.com/paper/2011.03432/) (2020)
- [Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated Learning](https://mlsanity.com/paper/2102.03109/) (2021)

## Built on by (most cited first, 1 of 1)

- [Asynchronous Federated Clustering with Unknown Number of Clusters](https://mlsanity.com/paper/2412.20341/) (2024)

## Abstract (arXiv)

In this paper we introduce a realistic and challenging, multi-source and multi-room acoustic environment and an improved algorithm for the estimation of source-dominated microphone clusters in acoustic sensor networks. Our proposed clustering method is based on a single microphone per node and on unsupervised clustered federated learning which employs a light-weight autoencoder model. We present an improved clustering control strategy that takes into account the variability of the acoustic scene and allows the estimation of a dynamic range of clusters using reduced amounts of training data. The proposed approach is optimized using clustering-based measures and validated via a network-wide classification task.