The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using
Federated XGBoost

The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost

16 July 2019

Linqi Song

Papers citing "The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost"

6 / 6 papers shown

Title
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates Chenyang Ma Xinchi Qiu Daniel J. Beutel Nicholas D. Lane FedML 18 12 0 15 Apr 2023
Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters -- A Bayesian Approach Pavol Mulinka Subham S. Sahoo Charalampos Kalalas P. H. Nardelli 22 3 0 07 Dec 2022
Federated XGBoost on Sample-Wise Non-IID Data Katelinh Jones Yuya Jeremy Ong Yi Zhou Nathalie Baracaldo FedML 38 7 0 03 Sep 2022
Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations Kennedy Edemacu Beakcheol Jang Jong Wook Kim 22 1 0 07 Feb 2022
Privacy-Preserving Machine Learning: Methods, Challenges and Directions Runhua Xu Nathalie Baracaldo J. Joshi 32 100 0 10 Aug 2021
SoK: Privacy-Preserving Collaborative Tree-based Model Learning Sylvain Chatel Apostolos Pyrgelis J. Troncoso-Pastoriza Jean-Pierre Hubaux 17 14 0 16 Mar 2021