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Blockchain-enabled asynchronous federated learning in edge computing

Liu, Yinghui, Qu, Youyang, Xu, Chenhao, Hao, Zhicheng and Gu, Bruce 2021, Blockchain-enabled asynchronous federated learning in edge computing, Sensors, vol. 21, no. 10, pp. 1-16, doi: 10.3390/s21103335.

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Title Blockchain-enabled asynchronous federated learning in edge computing
Author(s) Liu, Yinghui
Qu, YouyangORCID iD for Qu, Youyang orcid.org/0000-0002-2944-4647
Xu, Chenhao
Hao, Zhicheng
Gu, Bruce
Journal name Sensors
Volume number 21
Issue number 10
Article ID 3335
Start page 1
End page 16
Total pages 16
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2021-05-02
ISSN 1424-8220
1424-8220
Keyword(s) asynchronous convergence
blockchain
edge computing
federated learning
Summary The fast proliferation of edge computing devices brings an increasing growth of data, which directly promotes machine learning (ML) technology development. However, privacy issues during data collection for ML tasks raise extensive concerns. To solve this issue, synchronous federated learning (FL) is proposed, which enables the central servers and end devices to maintain the same ML models by only exchanging model parameters. However, the diversity of computing power and data sizes leads to a significant difference in local training data consumption, and thereby causes the inefficiency of FL. Besides, the centralized processing of FL is vulnerable to single-point failure and poisoning attacks. Motivated by this, we propose an innovative method, federated learning with asynchronous convergence (FedAC) considering a staleness coefficient, while using a blockchain network instead of the classic central server to aggregate the global model. It avoids real-world issues such as interruption by abnormal local device training failure, dedicated attacks, etc. By comparing with the baseline models, we implement the proposed method on a real-world dataset, MNIST, and achieve accuracy rates of 98.96% and 95.84% in both horizontal and vertical FL modes, respectively. Extensive evaluation results show that FedAC outperforms most existing models.
Language eng
DOI 10.3390/s21103335
Indigenous content off
Field of Research 0301 Analytical Chemistry
0805 Distributed Computing
0906 Electrical and Electronic Engineering
0502 Environmental Science and Management
0602 Ecology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30151588

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.