GoMORE: Global Model Reuse for Resource-Constrained Wireless Federated Learning-Reference-Cited by-同舟云学术

GoMORE: Global Model Reuse for Resource-Constrained Wireless Federated Learning

Published:2023-09 Issue:9 Volume:12 Page:1543-1547
ISSN:2162-2337
Container-title:IEEE Wireless Communications Letters
language:
Short-container-title:IEEE Wireless Commun. Lett.

Author:

Yao Jiacheng¹^ORCID,Yang Zhaohui²^ORCID,Xu Wei¹^ORCID,Chen Mingzhe³^ORCID,Niyato Dusit⁴^ORCID

Affiliation:

1. National Mobile Communications Research Laboratory, Southeast University, Nanjing, China

2. Zhejiang Laboratory, Hangzhou, China

3. Department of Electrical and Computer Engineering, Institute for Data Science and Computing, University of Miami, Coral Gables, FL, USA

4. School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore

Funder

National Key Research and Development Program

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

MOE Tier 1

National Research Foundation (NRF), Singapore, and Infocomm Media Development Authority under the Future Communications Research Development Programme

DSO National Laboratories through the AI Singapore Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/5962382/10244213/10142015.pdf?arnumber=10142015

Reference15 articles.

1. Federated Learning with Non-IID Data in Wireless Networks

2. Broadband Analog Aggregation for Low-Latency Federated Edge Learning

3. On the convergence of FedAvg on non-IID data;li;arXiv 1907 02189,2020

4. Transmission Power Control for Over-the-Air Federated Averaging at Network Edge

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

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