FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering
with Differential Privacy
FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering
with Differential Privacy
We study the problem of privacy-preserving $k$-means clustering in the horizontally federated setting. Existing federated approaches using secure computation, suffer from substantial overheads and do not offer output privacy. At the same time, differentially private (DP) $k$-means algorithms assume a trusted central curator and do not extend to federated settings. …