Learning with Impartiality to Walk on the Pareto Frontier of Fairness,
Privacy, and Utility

Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility

17 February 2023

Mohammad Yaghini

Franziska Boenisch

Nicolas Papernot

Papers citing "Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility"

10 / 10 papers shown

Title
Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy Rushabh Solanki Meghana Bhange Ulrich Aïvodji Elliot Creager 29 0 0 09 May 2025
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 47 0 0 20 Mar 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients Clément Lalanne Jean-Michel Loubes David Rodríguez-Vítores FedML 46 0 0 03 Feb 2025
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 39 0 0 03 Oct 2024
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning Luca Corbucci Mikko A. Heikkilä David Solans Noguero Anna Monreale Nicolas Kourtellis FedML 52 3 0 21 Jul 2024
Differentially Private Fair Binary Classifications Hrad Ghoukasian S. Asoodeh FaML 34 1 0 23 Feb 2024
Regulation Games for Trustworthy Machine Learning Mohammad Yaghini Patty Liu Franziska Boenisch Nicolas Papernot FaML 23 2 0 05 Feb 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 47 2 0 07 Dec 2023
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning Jiaqi Wang R. Schuster Ilia Shumailov David Lie Nicolas Papernot FedML 30 3 0 22 Sep 2022
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 182 154 0 26 Feb 2021