Joint Device Selection and Power Control for Wireless Federated Learning-Reference-Cited by-同舟云学术

Joint Device Selection and Power Control for Wireless Federated Learning

Published:2022-08 Issue:8 Volume:40 Page:2395-2410
ISSN:0733-8716
Container-title:IEEE Journal on Selected Areas in Communications
language:
Short-container-title:IEEE J. Select. Areas Commun.

Author:

Guo Wei¹^ORCID,Li Ran¹^ORCID,Huang Chuan¹^ORCID,Qin Xiaoqi²^ORCID,Shen Kaiming¹^ORCID,Zhang Wei³^ORCID

Affiliation:

1. School of Science and Engineering (SSE), The Chinese University of Hong Kong, Shenzhen, China

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

3. School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia

Funder

National Key Research and Development Program of China

Natural Science Foundation of China

Guangdong Provincial Key Laboratory of Future Networks of Intelligence

Basic Research Project of Hetao Shenzhen-Hong Kong (HK) Science and Technology (S&T) Cooperation Zone

Shenzhen Science and Innovation Fund

Australian Research Council’s Project funding scheme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/49/9830856/09793704.pdf?arnumber=9793704

Reference43 articles.

1. Optimal Power Allocation for Parameter Tracking in a Distributed Amplify-and-Forward Sensor Network

2. Client selection in federated learning: Convergence analysis and power-of-choice selection strategies;cho;arXiv 2010 01243,2020

3. FedPD: A federated learning framework with optimal rates and adaptivity to non-IID data;zhang;arXiv 2005 11418,2020

4. Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent;lian;Proc Adv Neural Inf Process Syst (NIPS),2017

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