Differentially Private Federated $k$-Means with Server-Side Data

25 Sept 2024 (modified: 21 Jan 2025)ICLR 2025 Conference Withdrawn SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Clustering, Differential Privacy, Federated Learning
TL;DR: We propose a federated and differentially private k-means clustering algorithm that leverages server-side data for initialization, achieving strong empirical performance with theoretical guarantees on convergence and cluster identification.
Abstract: Clustering has long been a cornerstone of data analysis. It 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.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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