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Gradient boosting decision tree (GBDT) is a powerful and widely-used machinelearning model, which has achieved state-of-the-art performance in manyacademic areas and production environment. However, communication overhead isthe main bottleneck in distributed training which can handle the massive datanowadays. In this paper, we propose two novel communication-efficient methodsover distributed dataset to mitigate this problem, a weighted sampling approachby which we can estimate the information gain over a small subset efficiently,and distributed protocols for weighted quantile problem used in approximatetree learning.

Communication-Efficient Weighted Sampling and Quantile Summary for GBDT