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Computation offloading for edge-assisted federated learning
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Ji, Zhongming, Chen, Li, Zhao, Nan, Chen, Yunfei, Wei, Guo and Yu, Richard F. (2021) Computation offloading for edge-assisted federated learning. IEEE Transactions on Vehicular Technology, 70 (9). pp. 9330-9344. doi:10.1109/TVT.2021.3098022 ISSN 0018-9545.
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Official URL: https://doi.org/10.1109/TVT.2021.3098022
Abstract
When applying machine learning techniques to the Internet of things, aggregating massive amount of data seriously reduce the system efficiency. To tackle this challenge, a distributed learning framework called federated learning has been proposed. Due to the parallel training structure, the performance of federated learning suffers from the straggler effect. In this paper, to mitigate the straggler effect, we propose a novel learning scheme, edge-assisted federated learning (EAFL), which utilizes edge computing to reduce the computational burdens for stragglers in federated learning. It enables stragglers to offload partial computation to the edge server, and leverages the server’s idle computing power to assist clients in model training. The offloading data size is optimized to minimize the learning delay of the system. Based on the optimized data size, a threshold-based offloading strategy for EAFL is proposed. Moreover, we extend EAFL to a dynamic scenario where clients may be offline after several update rounds. By grouping clients into different sets, we formulate the new EAFL delay optimization problem and derive the corresponding offloading strategy for the dynamic scenario. Simulation results are presented to show that EAFL has lower system delay than the original federated learning scheme.
Item Type: | Journal Article | ||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||||||
Library of Congress Subject Headings (LCSH): | Electronic data processing -- Distributed processing, Machine learning, Internet of things, Cloud computing, Federated database systems | ||||||||||||
Journal or Publication Title: | IEEE Transactions on Vehicular Technology | ||||||||||||
Publisher: | IEEE | ||||||||||||
ISSN: | 0018-9545 | ||||||||||||
Official Date: | September 2021 | ||||||||||||
Dates: |
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Volume: | 70 | ||||||||||||
Number: | 9 | ||||||||||||
Page Range: | pp. 9330-9344 | ||||||||||||
DOI: | 10.1109/TVT.2021.3098022 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Reuse Statement (publisher, data, author rights): | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
Date of first compliant deposit: | 19 July 2021 | ||||||||||||
Date of first compliant Open Access: | 20 July 2021 | ||||||||||||
RIOXX Funder/Project Grant: |
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