Efficient Federated Learning in Resource-Constrained Edge Intelligence Networks using Model Compression-Reference-Cited by-同舟云学术

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Efficient Federated Learning in Resource-Constrained Edge Intelligence Networks using Model Compression

Published:2023 Issue: Volume: Page:1-12
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Chen Chao¹^ORCID,Jiang Bohang¹^ORCID,Liu Shengli²^ORCID,Li Chuanhuang¹^ORCID,Wu Celimuge³^ORCID,Yin Rui²

Affiliation:

1. School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Hangzhou, China

2. School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, China

3. Graduate School of Informatics and Engineering, University of Electro-Communications, Japan

Funder

National Natural Science Foundation of China

JSPS KAKENHI

Zhejiang Provincial Natural Science Foundation of China

Fundamental Research Funds for the Provincial Universities of Zhejiang

Zhejiang Provincial Key Laboratory of New Network Standards and Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/4356907/10258354.pdf?arnumber=10258354

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Large-Scale Decentralized Asynchronous Federated Edge Learning with Device Heterogeneity;ICC 2024 - IEEE International Conference on Communications;2024-06-09

2. Quality-aware Client Selection and Resource Optimization for Federated Learning in Computing Networks;ICC 2024 - IEEE International Conference on Communications;2024-06-09

3. An Empirical Analysis and Resource Footprint Study of Deploying Large Language Models on Edge Devices;Proceedings of the 2024 ACM Southeast Conference on ZZZ;2024-04-18

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