Toward the Tradeoffs between Privacy, Fairness and Utility in Federated
Learning

Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning

30 November 2023

Gaolei Li

Papers citing "Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning"

6 / 6 papers shown

Title
RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility in Autonomous Vehicles Dawood Wasif T. Moore Jin-Hee Cho 40 0 0 20 Mar 2025
TowerDebias: A Novel Unfairness Removal Method Based on the Tower Property Norman Matloff Aditya Mittal 27 0 0 13 Nov 2024
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning Luca Corbucci Mikko A. Heikkilä David Solans Noguero Anna Monreale Nicolas Kourtellis FedML 44 3 0 21 Jul 2024
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data Georgi Ganev Bristena Oprisanu Emiliano De Cristofaro 37 57 0 23 Sep 2021
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 136 420 0 29 Nov 2018
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,082 0 24 Oct 2016