With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

Federated learning is generally used in tasks where labels are readily available (eg, next
word prediction). Relaxing this constraint requires design of unsupervised learning …

The shuffle model of differential privacy has attracted attention in the literature due to it being
a middle ground between the well-studied central and local models. In this work, we study …

Privacy-preserving data analysis is an emerging area that addresses the dilemma of
performing data analysis on user data while protecting users' privacy. In this paper, we …

We design a new algorithm for the Euclidean k-means problem that operates in the local
model of differential privacy. Unlike in the non-private literature, differentially private …

Differential privacy (DP) is a formal notion for quantifying the privacy loss of algorithms.
Algorithms in the central model of DP achieve high accuracy but make the strongest trust …

In many applications, multiple parties have private data regarding the same set of users but
on disjoint sets of attributes, and a server wants to leverage the data to train a model. To …

We study the differentially private (DP) $ k $-means and $ k $-median clustering problems of
$ n $ points in $ d $-dimensional Euclidean space in the massively parallel computation …

Abstract Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm
for allowing distributed clients to collaboratively train a machine learning model over scarce …

We study the private k-median and k-means clustering problem in d dimensional Euclidean
space. By leveraging tree embeddings, we give an efficient and easy to implement …