<ul data-eligibleForWebStory="true"><li>Researchers propose a new method for privacy-preserving $k$-means clustering in the horizontally federated setting.</li><li>Existing federated approaches for $k$-means clustering have issues with overheads and output privacy.</li><li>Differentially private $k$-means algorithms face challenges like a trusted curator or degraded utility due to added noise.</li><li>A new method is introduced that enhances both differential privacy and secure computation components.</li><li>The proposed design is faster, more private, and more accurate than previous approaches.</li><li>Utilizing the computational differentially private model, a secure aggregation-based approach achieves significant speed improvements.</li><li>The new method maintains and improves the utility compared to existing central models of differential privacy.</li></ul>

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

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