Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 7
2184-2191

Federated gradient boosting decision tree for non-IID dataset

Zhao Xue
Li Xiaohui
School of Electronics & Information Engineering, Liaoning University of Technology, Jinzhou Liaoning 121000, China
DOI: 10.19734/j.issn.1001-3695.2022.12.0764

Abstract

With the continuous rise of federated learning, gradient boosting decision tree(GBDT), as a traditional machine learning method, is gradually applied to federated learning to achieve ideal classification results. Aiming at the problems of the existing horizontal federated learning model of GBDT, such as the accuracy is greatly influenced by non-IID dataset, information leakage and high communication cost, the paper proposed a federated gradient boosting decision tree for non-IID dataset, called nFL-GBDT. Firstly, the algorithm calculated similar samples among the participants by utilizing the LSH, and using weighted gradient constructed the first tree. Secondly, a reliable third party calculated the global leaf weight that only needed one round of communication. Finally, the experimental analysis shows that the algorithm can protect the privacy of the original data, and its communication cost is lower than that of simFL and FederBoost. At the same time, the experiment divides three groups of public data sets according to the imbalance ratio. The results show that the accuracy of this algorithm is improved by 3.53%, 5.46% and 4.43% respectively compared with Individual, TFL and F-GBDT-G.

Foundation Support

国家自然科学基金青年科学基金资助项目(61802161)
辽宁省应用基础研究计划项目(2022JH2/101300278,2022JH2/101300279)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.12.0764
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 7
Section: Technology of Information Security
Pages: 2184-2191
Serial Number: 1001-3695(2023)07-040-2184-08

Publish History

[2023-02-13] Accepted Paper
[2023-07-05] Printed Article

Cite This Article

赵雪, 李晓会. 面向非独立同分布数据的联邦梯度提升决策树 [J]. 计算机应用研究, 2023, 40 (7): 2184-2191. (Zhao Xue, Li Xiaohui. Federated gradient boosting decision tree for non-IID dataset [J]. Application Research of Computers, 2023, 40 (7): 2184-2191. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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