Recent advances on distributed unsupervised learning

Distributed machine learning is a problem of inferring a desired relation when the training data is distributed throughout a network of agents (e.g. sensor networks,robotswarms,etc.).Atypicalproblemofunsupervisedlearningisclustering, that is grouping patterns based on some similarity/dissimilarity measures. Provided theyarehighlyscalable,fault-tolerantandenergyefficient,clusteringalgorithmscan be adopted in large-scale distributed systems. This work surveys the state-of-the-art in this field, presenting algorithms that solve the distributed clustering problem efficiently, with particular attention to the computation and clustering criteria.