PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks

5 April 2020

Papers citing "PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks"

7 / 7 papers shown

Title
Federated learning with differential privacy and an untrusted aggregator Kunlong Liu Trinabh Gupta 55 0 0 17 Dec 2023
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX Chengliang Zhang Junzhe Xia Baichen Yang Huancheng Puyang Wei Wang Ruichuan Chen Istemi Ekin Akkus Paarijaat Aditya Feng Yan FedML 53 39 0 04 May 2021
From Distributed Machine Learning to Federated Learning: A Survey Ji Liu Jizhou Huang Yang Zhou Xuhong Li Shilei Ji Haoyi Xiong Dejing Dou FedML OOD 56 244 0 29 Apr 2021
Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning Hangyu Zhu Rui Wang Yaochu Jin K. Liang Jianting Ning FedML 35 46 0 25 Nov 2020
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security Sheng Shen Tianqing Zhu Di Wu Wei Wang Wanlei Zhou FedML OOD 23 77 0 19 Oct 2020
Privacy-Preserving Machine Learning Training in Aggregation Scenarios Liehuang Zhu Xiangyun Tang Meng Shen Jie Zhang Xiaojiang Du 32 4 0 21 Sep 2020
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective Sin Kit Lo Qinghua Lu Chen Wang Hye-Young Paik Liming Zhu FedML 53 83 0 22 Jul 2020