REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning Over Mobile Devices-Reference-Cited by-同舟云学术

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REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning Over Mobile Devices

Published:2024-10 Issue:10 Volume:23 Page:9487-9501
ISSN:1536-1233
Container-title:IEEE Transactions on Mobile Computing
language:
Short-container-title:IEEE Trans. on Mobile Comput.

Author:

Li Yixuan¹^ORCID,Qin Xiaoqi¹^ORCID,Geng Jiaxiang¹^ORCID,Chen Rui²^ORCID,Hou Yanzhao¹^ORCID,Gong Yanmin³^ORCID,Pan Miao²^ORCID,Zhang Ping¹^ORCID

Affiliation:

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

2. Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA

3. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

Funder

National Key Research and Development Program of China

NSFC Project

National Science Foundation

JST ASPIRE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/7755/10663873/10433690-aam.pdf

Reference49 articles.

1. To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

2. Blockchain-Empowered Federated Learning for Healthcare Metaverses: User-Centric Incentive Mechanism With Optimal Data Freshness

3. Joint Device Selection and Power Control for Wireless Federated Learning

4. Clustered Federated Learning With Adaptive Local Differential Privacy on Heterogeneous IoT Data

5. Client selection in federated learning: Convergence analysis and power-of-choice selection strategies;Cho,2020

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