PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning

21 July 2024

Papers citing "PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning"

8 / 8 papers shown

Title
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning Kangkang Sun Xiaojin Zhang Xi Lin Gaolei Li Jing Wang Jianhua Li 25 4 0 30 Nov 2023
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
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning Sikha Pentyala Nicola Neophytou A. Nascimento Martine De Cock G. Farnadi 26 17 0 23 May 2022
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 64 181 0 06 Dec 2021
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 144 347 0 25 Sep 2021
Enforcing fairness in private federated learning via the modified method of differential multipliers Borja Rodríguez Gálvez Filip Granqvist Rogier van Dalen M. Seigel FedML 36 51 0 17 Sep 2021
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 186 432 0 04 Mar 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,187 0 23 Aug 2019