On the Convergence of Hierarchical Federated Learning with Gradient Quantization and Imperfect Transmission-Reference-Cited by-同舟云学术

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On the Convergence of Hierarchical Federated Learning with Gradient Quantization and Imperfect Transmission

Published:2024-04-14 Issue: Volume:abs/2006.10672 Page:7245-7249
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Sun Haofeng¹,Tian Hui¹,Ni Wanli²,Zheng Jingheng¹

Affiliation:

1. Beijing University of Posts and Telecommunications,State Key Laboratory of Networking and Switching Technology,Beijing,China,100876

2. Tsinghua University,Department of Electronic Engineering,Beijing,China,100084

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446709.pdf?arnumber=10446709

Reference21 articles.

1. A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems

2. Survey on Machine Learning for Intelligent End-to-End Communication Toward 6G: From Network Access, Routing to Traffic Control and Streaming Adaption

3. Federated Learning for 6G: Applications, Challenges, and Opportunities

4. Federated Learning in Mobile Edge Networks: A Comprehensive Survey

5. A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

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1. Green Edge AI: A Contemporary Survey;Proceedings of the IEEE;2024

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