Institute of Electrical and Electronics Engineers (IEEE)

IEEE Wireless Communications Letters

, volume 11 , issue 5 , pages 972-976

FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things

Anik Islam ¹

Ahmed I Amin ¹

Soo Yeon Shin ¹

Hide authors affiliations Show authors affiliations: 1 affiliation

Department of IT Convergence Engineering, WENS Laboratory, Kumoh National Institute of Technology, Gumi, South Korea |

Publication type: Journal Article

Publication date: 2022-05-01

Institute of Electrical and Electronics Engineers (IEEE)

IEEE Wireless Communications Letters

scimago Q1

wos Q1

SJR: 2.240

CiteScore: 10.5

Impact factor: 5.5

ISSN: 21622337, 21622345

DOI: 10.1109/lwc.2022.3151873

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Electrical and Electronic Engineering

Control and Systems Engineering

Abstract

This letter presents a federated learning-basd data-accumulation scheme that combines drones and blockchain for remote regions where Internet of Things devices face network scarcity and potential cyber threats. The scheme contains a two-phase authentication mechanism in which requests are first validated using a cuckoo filter, followed by a timestamp nonce. Secure accumulation is achieved by validating models using a Hampel filter and loss checks. To increase the privacy of the model, differential privacy is employed before sharing. Finally, the model is stored in the blockchain after consent is obtained from mining nodes. Experiments are performed in a proper environment, and the results confirm the feasibility of the proposed scheme.

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Islam A., Amin A. I., Shin S. Y. FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things // IEEE Wireless Communications Letters. 2022. Vol. 11. No. 5. pp. 972-976.

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RIS |

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RIS Copy

TY - JOUR

DO - 10.1109/lwc.2022.3151873

UR - https://doi.org/10.1109/lwc.2022.3151873

TI - FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things

T2 - IEEE Wireless Communications Letters

AU - Islam, Anik

AU - Amin, Ahmed I

AU - Shin, Soo Yeon

PY - 2022

DA - 2022/05/01

PB - Institute of Electrical and Electronics Engineers (IEEE)

SP - 972-976

IS - 5

VL - 11

SN - 2162-2337

SN - 2162-2345

ER -

BibTex |

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BibTex (up to 50 authors) Copy

@article{2022_Islam,

author = {Anik Islam and Ahmed I Amin and Soo Yeon Shin},

title = {FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things},

journal = {IEEE Wireless Communications Letters},

year = {2022},

volume = {11},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

month = {may},

url = {https://doi.org/10.1109/lwc.2022.3151873},

number = {5},

pages = {972--976},

doi = {10.1109/lwc.2022.3151873}

}

MLA

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MLA Copy

Islam, Anik, et al. “FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things.” IEEE Wireless Communications Letters, vol. 11, no. 5, May. 2022, pp. 972-976. https://doi.org/10.1109/lwc.2022.3151873.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Journal

IEEE Wireless Communications Letters

scimago Q1

wos Q1

SJR

2.240

CiteScore

10.5

Impact factor

5.5

ISSN

21622337 (Print)

21622345 (Electronic)