Balancing Privacy and Performance for Private Federated Learning Algorithms

11 April 2023

Papers citing "Balancing Privacy and Performance for Private Federated Learning Algorithms"

5 / 5 papers shown

Title
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 94 167 0 01 Mar 2023
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging Rui Yan Liangqiong Qu Qingyue Wei Shih-Cheng Huang Liyue Shen D. Rubin Lei Xing Yuyin Zhou FedML 70 86 0 17 May 2022
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 173 411 0 14 Jul 2021
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication Muah Kim Onur Gunlu Rafael F. Schaefer FedML 101 118 0 09 Feb 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,453 0 23 Jan 2020