HELCFL: High-Efficiency and Low-Cost Federated Learning in Heterogeneous Mobile-Edge Computing-Reference-Cited by-同舟云学术

HELCFL: High-Efficiency and Low-Cost Federated Learning in Heterogeneous Mobile-Edge Computing

Published:2022-03-14 Issue: Volume: Page:
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Container-title:2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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Author:

Cui Yangguang¹,Cao Kun²,Zhou Junlong³,Wei Tongquan¹

Affiliation:

1. East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China,200062

2. College of Information Science and Technology, Jinan University,Guangzhou,China,510632

3. School of Computer Science and Engineering, Nanjing University of Science and Technology,Nanjing,China,210094

Funder

National Key Research and Development Program of China

NSFC

China Postdoctoral Science Foundation

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

Reference19 articles.

1. Client Selection for Federated Learning with Hetero- geneous Resources in Mobile Edge;nishio;ICC,2019

2. A Survey of Hierarchical Energy Optimization for Mobile Edge Computing: A Perspective from End Devices to the Cloud;cong;ACM CSUR,2020

5. L4L: Experience-Driven Computational Resource Control in Federated Learning;zhan;IEEE TC,2021

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