Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off

10 February 2024

Papers citing "Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off"

7 / 7 papers shown

Title
Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey Mang Ye Wei Shen Bo Du E. Snezhko Vassili Kovalev PongChi Yuen FedML 68 3 0 25 May 2024
FedSheafHN: Personalized Federated Learning on Graph-structured Data Wenfei Liang Yanan Zhao Rui She Yiming Li Wee Peng Tay FedML 32 0 0 25 May 2024
Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations Chuan Chen Tianchi Liao Xiaojun Deng Zihou Wu Sheng Huang Zibin Zheng FedML 36 2 0 16 May 2024
Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges Chuan Chen Yuecheng Li Zhenpeng Wu Chengyuan Mai Youming Liu Yanming Hu Zibin Zheng Jiawen Kang 35 16 0 23 Apr 2023
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 138 268 0 25 Sep 2021
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication Muah Kim Onur Gunlu Rafael F. Schaefer FedML 90 117 0 09 Feb 2021
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm Canyi Lu Jiashi Feng Yudong Chen W. Liu Zhouchen Lin Shuicheng Yan 49 643 0 10 Apr 2018