PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

23 May 2022

Nicola Neophytou

Martine De Cock

Papers citing "PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning"

12 / 12 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 50 0 0 20 Mar 2025
Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI Dawood Wasif Dian Chen Sindhuja Madabushi Nithin Alluru T. Moore Jin-Hee Cho FedML 95 0 0 20 Mar 2025
Federated Fairness Analytics: Quantifying Fairness in Federated Learning Oscar Dilley Juan Marcelo Parra Ullauri Rasheed Hussain Dimitra Simeonidou FedML 42 0 0 15 Aug 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
Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives Linlin Wang Tianqing Zhu Wanlei Zhou Philip S. Yu 34 1 0 16 Jun 2024
Fairness and Privacy-Preserving in Federated Learning: A Survey Taki Hasan Rafi Faiza Anan Noor Tahmid Hussain Dong-Kyu Chae FedML 35 39 0 14 Jun 2023
Fair Differentially Private Federated Learning Framework Ayush K. Varshney Sonakshi Garg Arka P. Ghosh Sargam Gupta FedML 17 0 0 23 May 2023
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey Max Hort Zhenpeng Chen Jie M. Zhang Mark Harman Federica Sarro FaML AI4CE 33 160 0 14 Jul 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 69 181 0 06 Dec 2021
Unified Group Fairness on Federated Learning Fengda Zhang Kun Kuang Yuxuan Liu Long Chen Chao-Xiang Wu Fei Wu Jiaxun Lu Yunfeng Shao Jun Xiao FedML 63 20 0 09 Nov 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 48 52 0 17 Sep 2021
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications Matthias Paulik M. Seigel Henry Mason Dominic Telaar Joris Kluivers ... Dominic Hughes O. Javidbakht Fei Dong Rehan Rishi Stanley Hung FedML 180 126 0 16 Feb 2021