FederBoost：用于GBDT的私有联邦学习
FederBoost: Private Federated Learning for GBDT
AI总结：
FederBoost提出一种用于GBDT的私有联邦学习框架，支持垂直和水平数据划分，无需或仅需轻量密码操作，通过数据排序实现高效训练，在准确度上与集中式相当，速度提升4-5个数量级。
摘要：
联邦学习（FL）已成为机器学习和人工智能领域的一个新兴趋势。它允许多个参与者协作训练一个更好的全局模型，并提供了一种隐私感知的模型训练范式，因为它不要求参与者发布其原始训练数据。然而，现有的针对垂直划分数据或决策树的联邦学习解决方案需要繁重的密码学操作。在本文中，我们提出了一个名为FederBoost的框架，用于梯度提升决策树（GBDT）的私有联邦学习。它支持在垂直和水平划分的数据上运行GBDT。垂直FederBoost不需要任何密码学操作，而水平FederBoost仅需要轻量级的安全聚合。关键观察是，GBDT的整个训练过程依赖于数据的排序而非数值。我们完整实现了FederBoost，并通过在三个公共数据集上进行的大量实验评估了其效用和效率。我们的实验结果表明，垂直和水平FederBoost都达到了与集中式训练（所有数据收集在中央服务器中）相同的准确度水平，并且它们比用于联邦决策树训练的最先进解决方案快4-5个数量级；因此为工业应用提供了实用解决方案。
英文摘要：
Federated Learning (FL) has been an emerging trend in machine learning and artificial intelligence. It allows multiple participants to collaboratively train a better global model and offers a privacy-aware paradigm for model training since it does not require participants to release their original training data. However, existing FL solutions for vertically partitioned data or decision trees require heavy cryptographic operations. In this paper, we propose a framework named FederBoost for private federated learning of gradient boosting decision trees (GBDT). It supports running GBDT over both vertically and horizontally partitioned data. Vertical FederBoost does not require any cryptographic operation and horizontal FederBoost only requires lightweight secure aggregation. The key observation is that the whole training process of GBDT relies on the ordering of the data instead of the values. We fully implement FederBoost and evaluate its utility and efficiency through extensive experiments performed on three public datasets. Our experimental results show that both vertical and horizontal FederBoost achieve the same level of accuracy with 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 solutions for federated decision tree training; hence offering practical solutions for industrial applications.
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