Quantization Bits Allocation for Wireless Federated Learning-Reference-Cited by-同舟云学术

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Quantization Bits Allocation for Wireless Federated Learning

Published:2023-11 Issue:11 Volume:22 Page:8336-8351
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Lan Muhang¹^ORCID,Ling Qing²^ORCID,Xiao Song³,Zhang Wenyi⁴^ORCID

Affiliation:

1. School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China

2. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

3. Beijing Electronic Science and Technology Institute, Beijing, China

4. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7693/10314841/10091800.pdf?arnumber=10091800

Reference49 articles.

1. Federated Learning: Challenges, Methods, and Future Directions

2. Dynamic Aggregation for Heterogeneous Quantization in Federated Learning

3. MHAT: An efficient model-heterogenous aggregation training scheme for federated learning

4. Advances and Open Problems in Federated Learning

5. Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

Cited by 2 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. Joint Accuracy and Latency Optimization for Quantized Federated Learning in Vehicular Networks;IEEE Internet of Things Journal;2024-09-01

2. Fair Wireless Federated Learning Through the Identification of a Common Descent Direction;IEEE Communications Letters;2024-03

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