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Federated Learning (FL) has been an emerging trend in machine learning andartificial intelligence. It allows multiple participants to collaborativelytrain a better global model and offers a privacy-aware paradigm for modeltraining since it does not require participants to release their originaltraining data. However, existing FL solutions for vertically partitioned dataor decision trees require heavy cryptographic operations. In this paper, wepropose a framework named FederBoost for private federated learning of gradientboosting decision trees (GBDT). It supports running GBDT over both verticallyand horizontally partitioned data. Vertical FederBoost does not require anycryptographic operation and horizontal FederBoost only requires lightweightsecure aggregation. The key observation is that the whole training process ofGBDT relies on the ordering of the data instead of the values. We fullyimplement FederBoost and evaluate its utility and efficiency through extensiveexperiments performed on three public datasets. Our experimental results showthat both vertical and horizontal FederBoost achieve the same level of accuracywith centralized training, where all data are collected in a central server;and they are 4-5 orders of magnitude faster than the state-of-the-art solutionsfor federated decision tree training; hence offering practical solutions forindustrial application.

FederBoost: Private Federated Learning for GBDT