Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks-Reference-Cited by-同舟云学术

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Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks

Published:2023-10 Issue:10 Volume:34 Page:2629-2641
ISSN:1045-9219
Container-title:IEEE Transactions on Parallel and Distributed Systems
language:
Short-container-title:IEEE Trans. Parallel Distrib. Syst.

Author:

Yang Zhengjie¹^ORCID,Fu Sen¹^ORCID,Bao Wei¹^ORCID,Yuan Dong²^ORCID,Zomaya Albert Y.³^ORCID

Affiliation:

1. School of Computer Science, University of Sydney, Sydney, NSW, Australia

2. School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, Australia

3. School of Information Technologies, University of Sydney, Sydney, NSW, Australia

Funder

Australian Research Council Discovery Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/71/10201356/10180030.pdf?arnumber=10180030

Reference45 articles.

1. Accelerating Federated Learning via Momentum Gradient Descent

2. Why momentum really works;goh;Distillation,2017

3. On the convergence of Nesterov's accelerated gradient method in stochastic settings;assran;Proc 37th Int Conf Mach Learn,2020

4. Some methods of speeding up the convergence of iteration methods

5. Achieving linear speedup with partial worker participation in non-IID federated learning;yang;Proc Int Conf Learn Representations,2021

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1. Federated learning for green and sustainable 6G IIoT applications;Internet of Things;2024-04

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