<ul><li>We study the problem of privacy-preserving $k$-means clustering in the horizontally federated setting.</li><li>Existing federated approaches using secure computation suffer from substantial overheads and do not offer output privacy.</li><li>The work provides enhancements to both differentially private (DP) and secure computation components to achieve better speed, privacy, and accuracy.</li><li>By utilizing the computational DP model, a lightweight, secure aggregation-based approach is designed, achieving significant speed improvement and improved utility.</li></ul>

FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy

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