分布式设备协作聚类，保护隐私还高效。
Federated k-Means over Networks
- 基于邻近设备一致性约束的联邦k-means算法
- 仅交换聚合信息，实现隐私保护
- 适合分布式数据聚类场景
我们研究联邦聚类问题，即互连设备协作对各自私有本地数据集进行聚类。聚焦于基于k-means准则的硬聚类，将联邦k-means建模为广义总变差最小化（GTVMin）实例。由此提出一种联邦k-means算法：各设备通过求解带有正则项的局部k-means问题来更新其簇中心，该正则项强制相邻设备间的簇中心保持一致。该算法具有隐私友好性，因为仅需交换聚合信息。
原文摘要 · Abstract (English)
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.
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