Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations-Reference-Cited by-同舟云学术

Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations

Author:

Vu Tung Thanh¹^ORCID,Ngo Hien Quoc¹^ORCID,Dao Minh N.²^ORCID,Ngo Duy Trong³^ORCID,Larsson Erik G.⁴^ORCID,Le-Ngoc Tho⁵^ORCID

Affiliation:

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

2. School of Science, RMIT University, Melbourne, VIC, Australia

3. School of Engineering, The University of Newcastle, Callaghan, NSW, Australia

4. Department of Electrical Engineering, Linköping University, Linköping, Sweden

5. Department of Electrical and Computer Engineering, McGill University, Montreal, Canada

Funder

U.K. Research and Innovation Future Leaders Fellowships

ELLIIT

KAW Foundation

FMJH Program Gaspard Monge for Optimization and Operations Research and their Interactions with Data Science

Public Grant as part of the Investissement d’avenir Project

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications

Link

Reference60 articles.

2. Communication-efficient learning of deep networks from Decentralized data;mcmahan;Proc Int Conf Artif Intell Stat (AISTATS),2017

4. Asynchronous federated learning on heterogeneous devices: A survey;xu;ACM Comput Surveys,2021

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