Reward Teaching for Federated Multiarmed Bandits-Reference-Cited by-同舟云学术

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Reward Teaching for Federated Multiarmed Bandits

Published:2023 Issue: Volume:71 Page:4407-4422
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Shi Chengshuai¹^ORCID,Xiong Wei²,Shen Cong¹^ORCID,Yang Jing³^ORCID

Affiliation:

1. Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA

2. Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA

3. Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, USA

Funder

U.S. National Science Foundation

U.S. NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/78/10040758/10325655.pdf?arnumber=10325655

Reference62 articles.

1. Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits

2. The KL-UCB algorithm for bounded stochastic bandits and beyond;garivier;Proc 24th Annu Conf Learn Theory,0

3. Distributed Learning in Multi-Armed Bandit With Multiple Players

4. Minimax policies for adversarial and stochastic bandits;audibert;Proc 22nd Annu Conf Learn Theory (COLT),0

5. Federated multi-armed bandits under Byzantine attacks;demirel,0

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