Cloud-based Federated Boosting for Mobile Crowdsensing

Cloud-based Federated Boosting for Mobile Crowdsensing

9 May 2020

Yang Liu

ArXiv (abs)PDF HTML

Papers citing "Cloud-based Federated Boosting for Mobile Crowdsensing"

6 / 6 papers shown

Title
Distributed and Deep Vertical Federated Learning with Big Data Ji Liu Xuehai Zhou L. Mo Shilei Ji Yuan Liao Zhu Li Qinhua Gu Dejing Dou FedML 79 18 0 08 Mar 2023
Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations Kennedy Edemacu Beakcheol Jang Jong Wook Kim 26 1 0 07 Feb 2022
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization Lunchen Xie Jiaqi Liu Songtao Lu Tsung-Hui Chang Qingjiang Shi FedML 64 38 0 12 May 2021
SoK: Privacy-Preserving Collaborative Tree-based Model Learning Sylvain Chatel Apostolos Pyrgelis J. Troncoso-Pastoriza Jean-Pierre Hubaux 73 15 0 16 Mar 2021
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning Yuya Jeremy Ong Yi Zhou Nathalie Baracaldo Heiko Ludwig FedML 83 23 0 11 Dec 2020
FedML: A Research Library and Benchmark for Federated Machine Learning Chaoyang He Songze Li Jinhyun So Xiao Zeng Mi Zhang ... Yang Liu Ramesh Raskar Qiang Yang M. Annavaram Salman Avestimehr FedML 269 578 0 27 Jul 2020

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