Federated Learning approach for Spectral Clustering

Abstract

[Abstract]: Spectral clustering is a clustering paradigm that has been shown to be more effective in finding clusters with non-convex shapes than some traditional algorithms such as k-means. However, this algorithm is not directly applicable when the data is naturally distributed in different locations, as it happens in many Internet of Things scenarios. In this work, we propose a distributed spectral clustering to create a cooperative federated model to deal with those cases in which the data is distributed in different sites and with data privacy concerns. We demonstrate that sharing a minimal amount of information allows this distributed version of the spectral clustering to achieve good behavior for clustering several synthetic data sets.