The Current State and Challenges of Fairness in Federated Learning

Author:

Vucinich Sean1ORCID,Zhu Qiang2ORCID

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

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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