FedFair3: Unlocking Threefold Fairness in Federated Learning
Author:
Affiliation:
1. School of Computing and Informatics, University of Louisiana,Lafayette
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10622104/10622158/10622273.pdf?arnumber=10622273
Reference32 articles.
1. Oort: Efficient federated learning via guided participant selection;Lai,2021
2. Fair resource allocation in federated learning;Li;arXiv preprint,2019
3. Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
4. Communication-efficient learning of deep networks from decentralized data;McMahan;Artificial intelligence and statistics,2017
5. Communication-efficient algorithms for statistical optimization
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024
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