Unveiling Group-Specific Distributed Concept Drift: A Fairness
Imperative in Federated Learning

Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning

12 February 2024

Papers citing "Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning"

14 / 14 papers shown

Title
FAIR-FATE: Fair Federated Learning with Momentum Teresa Salazar Miguel X. Fernandes Helder Araújo Pedro Abreu FedML 47 19 0 27 Sep 2022
Federated Learning under Distributed Concept Drift Ellango Jothimurugesan Kevin Hsieh Jianyu Wang Gauri Joshi Phillip B. Gibbons FedML 68 47 0 01 Jun 2022
FairFed: Enabling Group Fairness in Federated Learning Yahya H. Ezzeldin Shen Yan Chaoyang He Emilio Ferrara A. Avestimehr FedML 63 206 0 02 Oct 2021
Concept drift detection and adaptation for federated and continual learning F. Casado Dylan Lema Marcos F. Criado R. Iglesias Carlos V. Regueiro S. Barro FedML 35 63 0 27 May 2021
On the Fairness of Generative Adversarial Networks (GANs) Patrik Kenfack Daniil Dmitrievich Arapovy Rasheed Hussain S. M. Ahsan Kazmi A. Khan GAN 40 22 0 01 Mar 2021
Towards Fair Deep Anomaly Detection Hongjing Zhang Ian Davidson FaML 81 39 0 29 Dec 2020
Fairness in Machine Learning: A Survey Simon Caton C. Haas FaML 90 630 0 04 Oct 2020
FAHT: An Adaptive Fairness-aware Decision Tree Classifier Wenbin Zhang Eirini Ntoutsi FaML 41 110 0 16 Jul 2019
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 244 8,856 0 25 Aug 2017
Fairness in Criminal Justice Risk Assessments: The State of the Art R. Berk Hoda Heidari S. Jabbari Michael Kearns Aaron Roth 49 994 0 27 Mar 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 295 2,109 0 24 Oct 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 194 4,301 0 07 Oct 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 380 17,399 0 17 Feb 2016
SMOTE: Synthetic Minority Over-sampling Technique Nitesh Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 329 25,569 0 09 Jun 2011