A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems

Chengzu DONG; Jingwen ZHOU; Aiting YAO; Zhiyu XU; Frank JIANG; Shiping CHEN; Xiao LIU

doi:10.1109/ICDMW60847.2023.00136

A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems

Chengzu DONG, Jingwen ZHOU, Aiting YAO, Zhiyu XU, Frank JIANG^*, Shiping CHEN, Xiao LIU

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

This paper develops a cutting-edge multimodal federated learning framework, integrated with distributed ledger technologies, designed specifically for UAV delivery scenarios. The framework adopts various data modalities, including user pictures, behavior, and location, to dynamically optimize delivery routes and schedules, thus enhancing both user privacy and security of the delivery process. By employing federated learning, this framework allows data to be processed locally on individual devices, significantly enhancing both user privacy and data integrity. The integration of distributed ledger technology ensures that all updates to the federated model are not only immutable and traceable, but also secure. Through comprehensive evaluations, our framework shows outstanding improvements in both the efficiency and security of UAV deliveries. These findings show the transformative potential of our approach to establish user-centric, efficient, and secured UAV delivery systems.

Original language	English
Title of host publication	Proceedings : 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023
Editors	Jihe WANG, Yi HE, Thang N. DINH, Christan GRANT, Meikang QIU, Witold PEDRYCZ
Publisher	IEEE
Pages	1032-1039
Number of pages	8
ISBN (Electronic)	9798350381641
ISBN (Print)	9798350381658
DOIs	https://doi.org/10.1109/ICDMW60847.2023.00136
Publication status	Published - 2023
Externally published	Yes
Event	23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 - Shanghai, China Duration: 1 Dec 2023 → 4 Dec 2023

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023
Country/Territory	China
City	Shanghai
Period	1/12/23 → 4/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Blockchain
Distributed ledger
Federated Learning
Internet of Things
Multi-Modality
UAV Delivery

Access to Document

10.1109/ICDMW60847.2023.00136

Cite this

DONG, C., ZHOU, J., YAO, A., XU, Z., JIANG, F., CHEN, S., & LIU, X. (2023). A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems. In J. WANG, Y. HE, T. N. DINH, C. GRANT, M. QIU, & W. PEDRYCZ (Eds.), Proceedings : 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 (pp. 1032-1039). (IEEE International Conference on Data Mining Workshops, ICDMW). IEEE. https://doi.org/10.1109/ICDMW60847.2023.00136

DONG, Chengzu ; ZHOU, Jingwen ; YAO, Aiting et al. / A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems. Proceedings : 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023. editor / Jihe WANG ; Yi HE ; Thang N. DINH ; Christan GRANT ; Meikang QIU ; Witold PEDRYCZ. IEEE, 2023. pp. 1032-1039 (IEEE International Conference on Data Mining Workshops, ICDMW).

@inproceedings{4632231cf57a448b8a68f7ec688369b2,

title = "A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems",

abstract = "This paper develops a cutting-edge multimodal federated learning framework, integrated with distributed ledger technologies, designed specifically for UAV delivery scenarios. The framework adopts various data modalities, including user pictures, behavior, and location, to dynamically optimize delivery routes and schedules, thus enhancing both user privacy and security of the delivery process. By employing federated learning, this framework allows data to be processed locally on individual devices, significantly enhancing both user privacy and data integrity. The integration of distributed ledger technology ensures that all updates to the federated model are not only immutable and traceable, but also secure. Through comprehensive evaluations, our framework shows outstanding improvements in both the efficiency and security of UAV deliveries. These findings show the transformative potential of our approach to establish user-centric, efficient, and secured UAV delivery systems.",

keywords = "Blockchain, Distributed ledger, Federated Learning, Internet of Things, Multi-Modality, UAV Delivery",

author = "Chengzu DONG and Jingwen ZHOU and Aiting YAO and Zhiyu XU and Frank JIANG and Shiping CHEN and Xiao LIU",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 ; Conference date: 01-12-2023 Through 04-12-2023",

year = "2023",

doi = "10.1109/ICDMW60847.2023.00136",

language = "English",

isbn = "9798350381658",

series = "IEEE International Conference on Data Mining Workshops, ICDMW",

publisher = "IEEE",

pages = "1032--1039",

editor = "Jihe WANG and Yi HE and DINH, {Thang N.} and Christan GRANT and Meikang QIU and Witold PEDRYCZ",

booktitle = "Proceedings : 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023",

}

DONG, C, ZHOU, J, YAO, A, XU, Z, JIANG, F, CHEN, S & LIU, X 2023, A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems. in J WANG, Y HE, TN DINH, C GRANT, M QIU & W PEDRYCZ (eds), Proceedings : 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023. IEEE International Conference on Data Mining Workshops, ICDMW, IEEE, pp. 1032-1039, 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023, Shanghai, China, 1/12/23. https://doi.org/10.1109/ICDMW60847.2023.00136

A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems. / DONG, Chengzu; ZHOU, Jingwen; YAO, Aiting et al.
Proceedings : 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023. ed. / Jihe WANG; Yi HE; Thang N. DINH; Christan GRANT; Meikang QIU; Witold PEDRYCZ. IEEE, 2023. p. 1032-1039 (IEEE International Conference on Data Mining Workshops, ICDMW).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems

AU - DONG, Chengzu

AU - ZHOU, Jingwen

AU - YAO, Aiting

AU - XU, Zhiyu

AU - JIANG, Frank

AU - CHEN, Shiping

AU - LIU, Xiao

PY - 2023

Y1 - 2023

N2 - This paper develops a cutting-edge multimodal federated learning framework, integrated with distributed ledger technologies, designed specifically for UAV delivery scenarios. The framework adopts various data modalities, including user pictures, behavior, and location, to dynamically optimize delivery routes and schedules, thus enhancing both user privacy and security of the delivery process. By employing federated learning, this framework allows data to be processed locally on individual devices, significantly enhancing both user privacy and data integrity. The integration of distributed ledger technology ensures that all updates to the federated model are not only immutable and traceable, but also secure. Through comprehensive evaluations, our framework shows outstanding improvements in both the efficiency and security of UAV deliveries. These findings show the transformative potential of our approach to establish user-centric, efficient, and secured UAV delivery systems.

AB - This paper develops a cutting-edge multimodal federated learning framework, integrated with distributed ledger technologies, designed specifically for UAV delivery scenarios. The framework adopts various data modalities, including user pictures, behavior, and location, to dynamically optimize delivery routes and schedules, thus enhancing both user privacy and security of the delivery process. By employing federated learning, this framework allows data to be processed locally on individual devices, significantly enhancing both user privacy and data integrity. The integration of distributed ledger technology ensures that all updates to the federated model are not only immutable and traceable, but also secure. Through comprehensive evaluations, our framework shows outstanding improvements in both the efficiency and security of UAV deliveries. These findings show the transformative potential of our approach to establish user-centric, efficient, and secured UAV delivery systems.

KW - Blockchain

KW - Distributed ledger

KW - Federated Learning

KW - Internet of Things

KW - Multi-Modality

KW - UAV Delivery

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DO - 10.1109/ICDMW60847.2023.00136

M3 - Conference paper (refereed)

AN - SCOPUS:85186144450

SN - 9798350381658

T3 - IEEE International Conference on Data Mining Workshops, ICDMW

SP - 1032

EP - 1039

BT - Proceedings : 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023

A2 - WANG, Jihe

A2 - HE, Yi

A2 - DINH, Thang N.

A2 - GRANT, Christan

A2 - QIU, Meikang

A2 - PEDRYCZ, Witold

PB - IEEE

T2 - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023

Y2 - 1 December 2023 through 4 December 2023

ER -

DONG C, ZHOU J, YAO A, XU Z, JIANG F, CHEN S et al. A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems. In WANG J, HE Y, DINH TN, GRANT C, QIU M, PEDRYCZ W, editors, Proceedings : 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023. IEEE. 2023. p. 1032-1039. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW60847.2023.00136

A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems

Abstract

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Bibliographical note

Keywords

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