Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach-Reference-Cited by-同舟云学术

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Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach

Published:2022-07-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)
language:
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Author:

Farooq Muhammad¹,Vu Tung T.²,Quoc Ngo Hien²,Tran Le-Nam¹

Affiliation:

1. University College Dublin,School of Electrical and Electronic Engineering,Ireland

2. Queen’s University Belfast,Institute of Electronics, Communications, and Information Technology (ECIT),Belfast,UK,BT3 9DT

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9833808/9833812/09833955.pdf?arnumber=9833955

Reference12 articles.

1. Cell-Free Massive MIMO for Wireless Federated Learning

2. Federated Learning Over Wireless Fading Channels

3. Energy-Efficient Multi-Cell Massive MIMO Subject to Minimum User-Rate Constraints

4. Max–Min Fair Transmit Precoding for Multi-Group Multicasting in Massive MIMO

5. Power Allocation for Energy Efficiency and Secrecy of Wireless Interference Networks

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. Massive MIMO for Serving Federated Learning and Non-Federated Learning Users;IEEE Transactions on Wireless Communications;2024-01

2. Approximate computing in B5G and 6G wireless systems: A survey and future outlook;Computer Networks;2023-09

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