Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables

22 May 2023

Papers citing "Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables"

6 / 6 papers shown

Title
OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning Sindhuja Madabushi A. Khan Haider Ali Jin-Hee Cho FedML VLM 34 0 0 22 Apr 2025
Tree-based Models for Vertical Federated Learning: A Survey Bingchen Qian Yuexiang Xie Yaliang Li Bolin Ding Jingren Zhou FedML 56 0 0 03 Apr 2025
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective Lei Yu Meng Han Yiming Li Changting Lin Yao Zhang ... Yan Liu Haiqin Weng Yuseok Jeon Ka-Ho Chow Stacy Patterson FedML 58 9 0 06 Feb 2024
Federated Boosted Decision Trees with Differential Privacy Samuel Maddock Graham Cormode Tianhao Wang Carsten Maple S. Jha FedML 19 29 0 06 Oct 2022
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization Xiaochen Li Yuke Hu Weiran Liu Hanwen Feng Li Peng Yuan Hong Kui Ren Zhan Qin FedML 121 26 0 04 Oct 2022
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 55 182 0 22 Apr 2021