FAIR-FATE: Fair Federated Learning with Momentum

FAIR-FATE: Fair Federated Learning with Momentum

27 September 2022

Miguel X. Fernandes

Papers citing "FAIR-FATE: Fair Federated Learning with Momentum"

10 / 10 papers shown

Title
Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Rongguang Ye Ming Tang FedML 48 0 0 30 Apr 2025
The Cost of Local and Global Fairness in Federated Learning Yuying Duan Gelei Xu Yiyu Shi Michael Lemmon FedML 39 0 0 27 Mar 2025
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning Chia-Yuan Wu Frank E. Curtis Daniel P. Robinson DD 28 0 0 14 Sep 2024
A Multivocal Literature Review on Privacy and Fairness in Federated Learning Beatrice Balbierer Lukas Heinlein Domenique Zipperling Niklas Kühl 19 0 0 16 Aug 2024
PraFFL: A Preference-Aware Scheme in Fair Federated Learning Rongguang Ye Wei-Bin Kou Ming Tang FedML 31 4 0 13 Apr 2024
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning Teresa Salazar Joao Gama Helder Araújo Pedro Abreu FaML FedML 29 2 0 12 Feb 2024
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications Azim Akhtarshenas Mohammad Ali Vahedifar Navid Ayoobi B. Maham Tohid Alizadeh Sina Ebrahimi David López-Pérez FedML 28 4 0 08 Oct 2023
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
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
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 177 1,031 0 29 Nov 2018