# Asynch-SGBDT: Asynchronous Parallel Stochastic Gradient Boosting Decision Tree based on Parameters Server

arXiv: https://arxiv.org/abs/1804.04659 · HTML: https://mlsanity.com/paper/1804.04659/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo](https://mlsanity.com/paper/2412.11578/) (2024)
- [SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint](https://mlsanity.com/paper/2503.13721/) (2025)

## Abstract (arXiv)

In AI research and industry, machine learning is the most widely used tool. One of the most important machine learning algorithms is Gradient Boosting Decision Tree, i.e. GBDT whose training process needs considerable computational resources and time. To shorten GBDT training time, many works tried to apply GBDT on Parameter Server. However, those GBDT algorithms are synchronous parallel algorithms which fail to make full use of Parameter Server. In this paper, we examine the possibility of using asynchronous parallel methods to train GBDT model and name this algorithm as asynch-SGBDT (asynchronous parallel stochastic gradient boosting decision tree). Our theoretical and experimental results indicate that the scalability of asynch-SGBDT is influenced by the sample diversity of datasets, sampling rate, step length and the setting of GBDT tree. Experimental results also show asynch-SGBDT training process reaches a linear speedup in asynchronous parallel manner when datasets and GBDT trees meet high scalability requirements.