Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning

11 September 2023

Papers citing "Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning"

8 / 8 papers shown

Title
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates Chuanyin Wang Yifei Zhang Neng Gao Qiang Luo FedML 60 0 0 12 Mar 2025
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off Yuecheng Li Lele Fu Tong Wang Jian Lou Bin Chen Lei Yang Zibin Zheng Zibin Zheng Chuan Chen FedML 65 4 0 10 Feb 2024
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 26 22 0 20 Jul 2023
BadVFL: Backdoor Attacks in Vertical Federated Learning Mohammad Naseri Yufei Han Emiliano De Cristofaro FedML AAML 24 11 0 18 Apr 2023
Learning to Generate Image Embeddings with User-level Differential Privacy Zheng Xu Maxwell D. Collins Yuxiao Wang Liviu Panait Sewoong Oh S. Augenstein Ting Liu Florian Schroff H. B. McMahan FedML 22 29 0 20 Nov 2022
Straggler-Resilient Personalized Federated Learning Isidoros Tziotis Zebang Shen Ramtin Pedarsani Hamed Hassani Aryan Mokhtari FedML 22 9 0 05 Jun 2022
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 178 154 0 26 Feb 2021
Mechanism Design in Large Games: Incentives and Privacy Michael Kearns Mallesh M. Pai Aaron Roth Jonathan R. Ullman 80 182 0 17 Jul 2012