FLAME: Differentially Private Federated Learning in the Shuffle Model

FLAME: Differentially Private Federated Learning in the Shuffle Model

17 September 2020

Ruixuan Liu

Yang Cao

Masatoshi Yoshikawa

Papers citing "FLAME: Differentially Private Federated Learning in the Shuffle Model"

8 / 8 papers shown

Title
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 53 0 0 21 Feb 2025
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning Saber Malekmohammadi Yaoliang Yu Yang Cao FedML 88 5 0 17 Feb 2025
Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy Shuangqing Xu Yifeng Zheng Zhongyun Hua FedML 19 2 0 04 Oct 2024
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence Shuya Feng Meisam Mohammady Hanbin Hong Shenao Yan Ashish Kundu Binghui Wang Yuan Hong FedML 41 3 0 20 Jul 2024
Practical Vertical Federated Learning with Unsupervised Representation Learning Zhaomin Wu Yue Liu Bingsheng He FedML 30 37 0 13 Aug 2022
Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model Jacob Imola Takao Murakami Kamalika Chaudhuri 19 22 0 03 May 2022
EasyFL: A Low-code Federated Learning Platform For Dummies Weiming Zhuang Xin Gan Yonggang Wen Shuai Zhang FedML 19 46 0 17 May 2021
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 141 420 0 29 Nov 2018