用少量服务器数据提升私密联邦k均值聚类效果，兼顾隐私与性能。
Differentially Private Federated $k$-Means Clustering with Server-Side Data
- 利用少量服务器数据优化初始化，解决私密聚类难题
- 在合成与真实数据上实现优异聚类效果，收敛速度快
- 适合关注隐私保护的边缘计算场景，如医疗或金融数据
聚类是数据分析的核心方法，特别适用于从持续生成的大规模无标签数据中发现结构化子群。然而，随着数据越来越多地分布在边缘设备上，传统聚类方法难以适用，且隐私问题限制了数据集中传输。为此，我们提出FedDP-KMeans，一种完全联邦且具备差分隐私的k均值聚类算法。该方法利用（可能少量且分布外的）服务器端数据克服私密聚类的主要挑战：良好初始化的缺失。结合此初始化与简单的联邦差分隐私Lloyds算法，我们的方法在合成和真实世界基准任务上表现卓越。我们还提供了理论分析，给出了收敛速度和聚类识别成功率的边界。
原文摘要 · Abstract (English)
Clustering is a cornerstone of data analysis that is particularly suited to identifying coherent subgroups or substructures in unlabeled data, as are generated continuously in large amounts these days. However, in many cases traditional clustering methods are not applicable, because data are increasingly being produced and stored in a distributed way, e.g. on edge devices, and privacy concerns prevent it from being transferred to a central server. To address this challenge, we present FedDP-KMeans, a new algorithm for $k$-means clustering that is fully-federated as well as differentially private. Our approach leverages (potentially small and out-of-distribution) server-side data to overcome the primary challenge of differentially private clustering methods: the need for a good initialization. Combining our initialization with a simple federated DP-Lloyds algorithm we obtain an algorithm that achieves excellent results on synthetic and real-world benchmark tasks. We also provide a theoretical analysis of our method that provides bounds on the convergence speed and cluster identification success.
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