Confederated Learning: Federated Learning With Decentralized Edge Servers-Reference-Cited by-同舟云学术

Confederated Learning: Federated Learning With Decentralized Edge Servers

Published:2023 Issue: Volume:71 Page:248-263
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Wang Bin¹^ORCID,Fang Jun¹^ORCID,Li Hongbin²^ORCID,Yuan Xiaojun¹^ORCID,Ling Qing³^ORCID

Affiliation:

1. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China

2. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA

3. School of Computer Science and Engineering and Guangdong Provincial Key Laboratory of Computational Science, Sun Yat-Sen University, Guangdong, China

Funder

National Natural Science Foundation of China

National Science Foundation

Sichuan Science and Technology Program

Basic and Applied Basic Research Foundation of Guangdong Province

Guangdong Provincial Key Laboratory of Computational Science

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/78/10040758/10035442-aam.pdf

Reference41 articles.

1. Device Scheduling with Fast Convergence for Wireless Federated Learning

2. A Proximal ADMM for Decentralized Composite Optimization

3. Scheduling Policies for Federated Learning in Wireless Networks

4. Hybrid ADMM: a unifying and fast approach to decentralized optimization

5. Gossip Learning as a Decentralized Alternative to Federated Learning

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