Part of Proceedings of Machine Learning and Systems 5 (MLSys 2023) mlsys2023
Qinbin Li, WU ZHAOMIN, Yanzheng Cai, yuxuan han, Ching Man Yung, Tianyuan Fu, Bingsheng He
While the quality of machine learning services largely relies on the volume of training data, data regulations such as the General Data Protection Regulation (GDPR) impose stringent requirements on data transfer. Federated learning has emerged as a popular approach for enabling collaborative machine learning without sharing raw data. To facilitate the rapid development of federated learning, efficient and user-friendly federated learning systems are essential. Despite many existing federated learning systems designed for deep learning, tree-based federated learning systems have not been well exploited. This paper presents a tree-based federated learning system under a histogram-sharing scheme, named FedTree, that supports both horizontal and vertical federated training of GBDTs with configurable privacy protection techniques. Our extensive experiments show that FedTree achieves competitive accuracy to centralized training while incurring much less computational cost than the other generic federated learning systems.