The Current State and Challenges of Fairness in Federated Learning
Author:
Affiliation:
1. Center for Academic Innovation, University of Michigan, Ann Arbor, MI, USA
2. Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, MI, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10183984.pdf?arnumber=10183984
Reference60 articles.
1. Federated Learning in Mobile Edge Networks: A Comprehensive Survey
2. New metrics to evaluate the performance and fairness of personalized federated learning;divi;arXiv 2107 13173,2021
3. Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
4. Improving Fairness for Data Valuation in Horizontal Federated Learning
5. Fairness in federated learning for spatial–temporal applications;mashhadi;arXiv 2201 06598,2022
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