Unleashing Edgeless Federated Learning With Analog Transmissions-Reference-Cited by-同舟云学术

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Unleashing Edgeless Federated Learning With Analog Transmissions

Published:2024 Issue: Volume:72 Page:774-791
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Yang Howard H.¹^ORCID,Chen Zihan²^ORCID,Quek Tony Q. S.³^ORCID

Affiliation:

1. Zhejiang University/University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining, China

2. Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore

3. Singapore University of Technology and Design, Singapore

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Zhejiang—Singapore Innovation and AI Joint Research Lab

Zhejiang Lab Open Research Project

National Research Foundation, Singapore and Infocomm Media Development Authority

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/78/10347386/10388219.pdf?arnumber=10388219

Reference55 articles.

1. Communication-efficient learning of deep networks from decentralized data;McMahan,2017

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

3. Wireless Network Intelligence at the Edge

4. Resource Consumption for Supporting Federated Learning in Wireless Networks

5. Adaptive Hierarchical Federated Learning over Wireless Networks

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