Differentially Private Federated Clustering over Non-IID Data

TL;DR

This paper introduces DP-FedC, a differentially private federated clustering algorithm designed for non-IID data, offering theoretical privacy and convergence guarantees, and demonstrating superior performance over existing methods on real datasets.

Contribution

The paper reformulates federated clustering into a non-convex optimization problem and proposes a novel DP-FedC algorithm with theoretical privacy and convergence analysis for non-IID data.

Findings

DP-FedC outperforms state-of-the-art algorithms on real datasets.

Theoretical analysis confirms privacy protection and convergence.

Effective clustering results with privacy guarantees in federated settings.

Abstract

In this paper, we investigate federated clustering (FedC) problem, that aims to accurately partition unlabeled data samples distributed over massive clients into finite clusters under the orchestration of a parameter server, meanwhile considering data privacy. Though it is an NP-hard optimization problem involving real variables denoting cluster centroids and binary variables denoting the cluster membership of each data sample, we judiciously reformulate the FedC problem into a non-convex optimization problem with only one convex constraint, accordingly yielding a soft clustering solution. Then a novel FedC algorithm using differential privacy (DP) technique, referred to as DP-FedC, is proposed in which partial clients participation and multiple local model updating steps are also considered. Furthermore, various attributes of the proposed DP-FedC are obtained through theoretical analyses of privacy protection and convergence rate, especially for the case of non-identically and independently distributed (non-i.i.d.) data, that ideally serve as the guidelines for the design of the proposed DP-FedC. Then some experimental results on two real datasets are provided to demonstrate the efficacy of the proposed DP-FedC together with its much superior performance over some state-of-the-art FedC algorithms, and the consistency with all the presented analytical results.