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

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Massive MIMO for Serving Federated Learning and Non-Federated Learning Users

Published:2024-01 Issue:1 Volume:23 Page:247-262
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Farooq Muhammad¹^ORCID,Vu Tung Thanh²^ORCID,Ngo Hien Quoc²^ORCID,Tran Le-Nam³^ORCID

Affiliation:

1. School of Computer Science, University College Dublin, Dublin 04, Ireland

2. Institute of Electronics, Communications and Information Technology, Queen’s University Belfast, Belfast, U.K.

3. School of Electrical and Electronic Engineering, University College Dublin, Dublin 04, Ireland

Funder

Science Foundation Ireland

U.K. Research and Innovation Future Leaders Fellowships

Excellence Center at Linköping – Lund in Information Technology

Knut and Alice Wallenberg (KAW) Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7693/10384459/10132372.pdf?arnumber=10132372

Reference37 articles.

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

2. A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

3. People-centric sensing in assistive healthcare: Privacy challenges and directions

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

5. Bringing Computation Closer toward the User Network: Is Edge Computing the Solution?

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