We study federated clustering, where interconnected devices collaboratively cluster the data points of private local datasets. Focusing on hard clustering via the k-means principle, we formulate federated k-means as an instance of generalized total variation minimization (GTVMin). This leads to a federated k-means algorithm in which each device updates its local cluster centroids by solving a regularized k-means problem with a regularizer that enforces consistency between neighbouring devices. The resulting algorithm is privacy-friendly, as only aggregated information is exchanged.

翻译：本研究探讨联邦聚类问题，即互联设备通过协作对其私有本地数据集中的数据点进行聚类。聚焦于基于k-Means原理的硬聚类方法，我们将联邦k-Means构建为广义全变分最小化（GTVMin）的一个实例。由此推导出一种联邦k-Means算法：各设备通过求解带正则项的k-Means问题来更新本地聚类中心，该正则项用于强制相邻设备间的一致性。所提出的算法具有隐私友好特性，因其仅交换聚合信息。