  For high-dimensional data, there are huge communication costs for distributed
GBDT because the communication volume of GBDT is related to the number of
features. To overcome this problem, we propose a novel gradient boosting
algorithm, the Gradient Boosting Untrained Neural Network(GBUN). GBUN ensembles
the untrained randomly generated neural network that softly distributes data
samples to multiple neuron outputs and dramatically reduces the communication
costs for distributed learning. To avoid creating huge neural networks for
high-dimensional data, we extend Simhash algorithm to mimic forward calculation
of the neural network. Our experiments on multiple public datasets show that
GBUN is as good as conventional GBDT in terms of prediction accuracy and much
better than it in scaling property for distributed learning. Comparing to
conventional GBDT varieties, GBUN speeds up the training process up to 13 times
on the cluster with 64 machines, and up to 4614 times on the cluster with
100KB/s network bandwidth. Therefore, GBUN is not only an efficient distributed
learning algorithm but also has great potentials for federated learning.
