Federated Boosted Decision Trees with Differential Privacy

Federated Boosted Decision Trees with Differential Privacy

6 October 2022

Papers citing "Federated Boosted Decision Trees with Differential Privacy"

11 / 11 papers shown

Title
Bilateral Differentially Private Vertical Federated Boosted Decision Trees Bokang Zhang Zhikun Zhang Haodong Jiang Yong-Jin Liu Lihao Zheng Yuxiao Zhou Shuaiting Huang Junfeng Wu FedML 77 0 0 30 Apr 2025
Privacy-preserving federated prediction of pain intensity change based on multi-center survey data Supratim Das Mahdie Rafie Paula Kammer S. Skou D. T. Grønne ... Hans-Helmut Konig Md Shihab Ullaha Niklas Probul Jan Baumbacha L. Baumbach OOD FedML 38 0 0 12 Sep 2024
Cross-silo Federated Learning with Record-level Personalized Differential Privacy Junxu Liu Jian Lou Li Xiong Jinfei Liu Xiaofeng Meng 31 5 0 29 Jan 2024
FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting Meenatchi Sundaram Muthu Selva Annamalai Igor Bilogrevic Emiliano De Cristofaro 34 1 0 28 Nov 2023
Effective and Efficient Federated Tree Learning on Hybrid Data Qinbin Li Chulin Xie Xiaojun Xu Xiaoyuan Liu Ce Zhang Bo-wen Li Bingsheng He D. Song FedML 21 3 0 18 Oct 2023
A Survey for Federated Learning Evaluations: Goals and Measures Di Chai Leye Wang Liu Yang Junxue Zhang Kai Chen Qian Yang ELM FedML 17 21 0 23 Aug 2023
DPM: Clustering Sensitive Data through Separation Yara Schutt Johannes Liebenow Tanya Braun Marcel Gehrke Florian Thaeter Esfandiar Mohammadi 17 0 0 06 Jul 2023
Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables Yifeng Zheng Shuangqing Xu Songlei Wang Yan Gao Zhongyun Hua FedML 18 10 0 22 May 2023
Deep incremental learning models for financial temporal tabular datasets with distribution shifts Thomas Wong Mauricio Barahona OOD AIFin AI4TS 18 0 0 14 Mar 2023
Private Boosted Decision Trees via Smooth Re-Weighting Vahid R. Asadi M. Carmosino Mohammad Jahanara Akbar Rafiey Bahar Salamatian 22 2 0 29 Jan 2022
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 182 194 0 26 Feb 2021