Energy-Efficient and Privacy-Preserved Incentive Mechanism for Federated Learning in Mobile Edge Computing-Reference-Cited by-同舟云学术

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Energy-Efficient and Privacy-Preserved Incentive Mechanism for Federated Learning in Mobile Edge Computing

Published:2023-05-28 Issue: Volume: Page:
ISSN:
Container-title:ICC 2023 - IEEE International Conference on Communications
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Author:

Liu Jingyuan¹,Chang Zheng¹,Min Geyong²,Zhang Yan³

Affiliation:

1. School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731

2. University of Exeter,Department of Computer Science,Exeter,U.K.,EX4 4QF

3. University of Oslo,Department of Informatics,Norway

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10278505/10278554/10279757.pdf?arnumber=10279757

Reference18 articles.

1. Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning

2. Incentivize to Build: A Crowdsourcing Framework for Federated Learning

3. Spectrum sharing and resource allocation for energy-efficient heterogeneous cognitive radio networks with femtocells

4. On the convergence of local descent methods in federated learning;haddadpour;ArXiv Preprint,2019

5. Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing

Cited by 1 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. Contract-Based Incentive Mechanism for Federated Learning in Edge Computing System;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21

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