arXiv:2102.03109
공식 코드 · Papers with Code
Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated Learning
원문 초록
In this paper we present a privacy-aware method for estimating source-dominated microphone clusters in the context of acoustic sensor networks (ASNs). The approach is based on clustered federated learning which we adapt to unsupervised scenarios by employing a light-weight autoencoder model. The model is further optimized for training on very scarce data. In order to best harness the benefits of clustered microphone nodes in ASN applications, a method for the computation of cluster membership values is introduced. We validate the performance of the proposed approach using clustering-based measures and a network-wide classification task.
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A new type of federated clustering: A non-model-sharing approach
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Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
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