Towards Multi-Objective Statistically Fair Federated Learning

24 January 2022

Papers citing "Towards Multi-Objective Statistically Fair Federated Learning"

9 / 9 papers shown

Title
Federated Communication-Efficient Multi-Objective Optimization Baris Askin Pranay Sharma Gauri Joshi Carlee Joe-Wong FedML 64 1 0 21 Oct 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 Multi-Objective Learning Haibo Yang Zhuqing Liu Jia-Wei Liu Chaosheng Dong Michinari Momma FedML 23 7 0 15 Oct 2023
Mitigating Group Bias in Federated Learning: Beyond Local Fairness G. Wang Ali Payani Myungjin Lee Ramana Rao Kompella FedML 32 8 0 17 May 2023
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning Yan Kang Hanlin Gu Xingxing Tang Yuanqin He Yuzhu Zhang Jinnan He Yuxing Han Lixin Fan Kai Chen Qiang Yang FedML 65 18 0 29 Apr 2023
FAIR-FATE: Fair Federated Learning with Momentum Teresa Salazar Miguel X. Fernandes Helder Araújo Pedro Abreu FedML 30 18 0 27 Sep 2022
The Future of Digital Health with Federated Learning Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletari H. Roth ... Ronald M. Summers Andrew Trask Daguang Xu Maximilian Baust M. Jorge Cardoso OOD 174 1,705 0 18 Mar 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,203 0 23 Aug 2019