Energy Aware Task Allocation for Semi-Asynchronous Mobile Edge Learning-Reference-Cited by-同舟云学术

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Energy Aware Task Allocation for Semi-Asynchronous Mobile Edge Learning

Published:2023-12 Issue:4 Volume:7 Page:1766-1777
ISSN:2473-2400
Container-title:IEEE Transactions on Green Communications and Networking
language:
Short-container-title:IEEE Trans. on Green Commun. Netw.

Author:

Mohammad Umair¹^ORCID,Sorour Sameh²^ORCID,Hefeida Mohamed³^ORCID

Affiliation:

1. School of Computing and Information Sciences, Florida International University, Miami, FL, USA

2. School of Computing, Queen’s University, Kingston, Canada

3. Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

Funder

U.S. Department of Transportation’s University Transportation Center Program (USDOT UTC) through the Pacific Northwest Regional University Transportation Center

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Renewable Energy, Sustainability and the Environment

Link

http://xplorestaging.ieee.org/ielx7/7511293/10325381/10044266.pdf?arnumber=10044266

Reference27 articles.

1. A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

2. Demonstration of Federated Learning in a Resource-Constrained Networked Environment

3. Adaptive Task Allocation for Mobile Edge Learning

4. Energy Efficient Federated Learning Over Wireless Communication Networks

5. Demo abstract: Distributed machine learning at resource-limited edge nodes

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