A Random Access Scheme for Federated Learning Over Massive MIMO Systems-Reference-Cited by-同舟云学术

A Random Access Scheme for Federated Learning Over Massive MIMO Systems

Published:2023-11-01 Issue:21 Volume:10 Page:19027-19042
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Han Huimei¹^ORCID,Zhao Jun²^ORCID,Zhou Xinyu²

Affiliation:

1. College of Information Engineering, Key Laboratory of Communication Networks and Applications of Zhejiang Province, Zhejiang University of Technology, Hangzhou, P.R. China

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

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Singapore Ministry of Education Academic Research Fund

NTU-Wallenberg AI, Autonomous Systems and Software Program (WASP) Joint Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10288305/10130012.pdf?arnumber=10130012

Reference37 articles.

1. Collaborative Machine Learning at the Wireless Edge with Blind Transmitters

2. Ultra-Massive MIMO Channel Modeling for Graphene-Enabled Terahertz-Band Communications

3. A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems

4. Federated Learning via Over-the-Air Computation

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. Best Response Dynamics Convergence for Generalized Nash Equilibrium Problems: An Opportunity for Autonomous Multiple Access Design in Federated Learning;IEEE Internet of Things Journal;2024-05-15