Device Selection and Resource Allocation for Layerwise Federated Learning in Wireless Networks

In this article, we study device selection and resource allocation (DSRA) for layerwise federated learning (FL) in wireless networks. For effective learning, DSRA should be carefully determined considering the characteristics of both layerwise FL and wireless networks. To address this, we propose a...

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Bibliographic Details
Published inIEEE systems journal Vol. 16; no. 4; pp. 6441 - 6444
Main Author Lee, Hyun-Suk
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Bandwidth
Collaborative work
Distributed learning
Federated learning
federated learning (FL)
Optimization
Performance evaluation
Resource allocation
Resource management
selection
Wireless networks
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Summary:In this article, we study device selection and resource allocation (DSRA) for layerwise federated learning (FL) in wireless networks. For effective learning, DSRA should be carefully determined considering the characteristics of both layerwise FL and wireless networks. To address this, we propose a DSRA algorithm for layerwise FL, called LAFLAS, that maximizes the total average number of the shallow and entire parameter transmissions over time in FL while guaranteeing the ratio between their numbers. Through experiments, we show that the model trained by our LAFLAS outperforms those by state-of-the-art algorithms, which demonstrates the effectiveness of DSRA by LAFLAS.
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2022.3169461