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Distributed Learning with Sublinear Communication
[article]
2019
arXiv
pre-print
In distributed statistical learning, N samples are split across m machines and a learner wishes to use minimal communication to learn as well as if the examples were on a single machine. This model has received substantial interest in machine learning due to its scalability and potential for parallel speedup. However, in high-dimensional settings, where the number examples is smaller than the number of features ("dimension"), the speedup afforded by distributed learning may be overshadowed by
arXiv:1902.11259v2
fatcat:6a4ndvdsjzg7ngcir72ifua7ei