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