Federated Learning-Based Approach for Heterogeneous Task Scheduling in Edge Computing Environments

Alsalem, L. and Djemame, K. orcid.org/0000-0001-5811-5263 (2025) Federated Learning-Based Approach for Heterogeneous Task Scheduling in Edge Computing Environments. In: 2024 IEEE/ACM 17th International Conference on Utility and Cloud Computing (UCC). 17th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2024), 16-19 Dec 2024, Sharjah, UAE. IEEE , pp. 509-516. ISBN 979-8-3503-6721-8

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Item Type: Proceedings Paper
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Keywords: edge computing, task scheduling, federated learning, deep Q network
Dates:
  • Accepted: 17 October 2024
  • Published (online): 23 April 2025
  • Published: 23 April 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Distributed Systems & Services
Depositing User: Symplectic Publications
Date Deposited: 20 Dec 2024 10:41
Last Modified: 25 Apr 2025 09:34
Published Version: https://ieeexplore.ieee.org/document/10971764
Status: Published
Publisher: IEEE
Identification Number: 10.1109/UCC63386.2024.00079
Open Archives Initiative ID (OAI ID):

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