Byzantine Resilient Distributed Clustering with Redundant Data Assignment

In this paper, we present robust variants of distributed clustering algorithms for large datasets distributed across multiple machines in the presence of Byzantines. We propose a redundant data assignment scheme that enables us to obtain global information about the entire dataset for clustering purposes even when some machines are adversarial in nature. Simulation results show that the distributed algorithms based on the proposed assignment scheme provide good-quality solutions for a variety of clustering problems.