Communication-Efficient Computation on Distributed Noisy Datasets

This paper gives a first attempt to answer the following general question: Given a set of machines connected by a point-to-point communication network, each having a noisy dataset, how can we perform communication-efficient statistical estimations on the union of these datasets? Here 'noisy' means that a real-world entity may appear in different forms in different datasets, but those variants should be considered as the same universe element when performing statistical estimations. We give a first set of communicationefficient solutions for statistical estimations on distributed noisy datasets, including algorithms for distinct elements, L-0-sampling, heavy hitters, frequency moments and empirical entropy.