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An Efficient Learning Framework For Federated XGBoost Using Secret
  Sharing And Distributed Optimization

An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization

12 May 2021
Lunchen Xie
Jiaqi Liu
Songtao Lu
Tsung-Hui Chang
Qingjiang Shi
    FedML
ArXivPDFHTML

Papers citing "An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization"

4 / 4 papers shown
Title
Blind Federated Learning without initial model
Blind Federated Learning without initial model
Jose L. Salmeron
Irina Arévalo
FedML
32
7
0
24 Apr 2024
Privet: A Privacy-Preserving Vertical Federated Learning Service for
  Gradient Boosted Decision Tables
Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables
Yifeng Zheng
Shuangqing Xu
Songlei Wang
Yan Gao
Zhongyun Hua
FedML
28
10
0
22 May 2023
A Survey of Trustworthy Federated Learning with Perspectives on
  Security, Robustness, and Privacy
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Yifei Zhang
Dun Zeng
Jinglong Luo
Zenglin Xu
Irwin King
FedML
84
48
0
21 Feb 2023
Vertical Federated Learning: Concepts, Advances and Challenges
Vertical Federated Learning: Concepts, Advances and Challenges
Yang Liu
Yan Kang
Tianyuan Zou
Yanhong Pu
Yuanqin He
Xiaozhou Ye
Ye Ouyang
Yaqin Zhang
Qian Yang
FedML
64
162
0
23 Nov 2022
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