Blockchain and Federated Learning in Edge-Fog-Cloud Computing Environments for Smart Logistics
(2025) In Mesopotamian Journal of CyberSecurity 5(2). p.735-769- Abstract
The rapid growth of smart logistics, driven by IoT devices and data-intensive applications, necessitates secure, scalable, and efficient computing frameworks. As the edge-fog-cloud (EFC) paradigm supports real-time data processing, it faces significant security threats and attacks, including privacy risks, data breaches, and unauthorized access. To address these security threats and attacks, blockchain and federated learning (FL) have gained popularity as potential solutions in EFC computing environments for smart logistics. This survey reviews the current landscape in EFC computing environments for smart logistics, highlighting the existing benefits and challenges identified in 134 research studies published between January 2023 and... (More)
The rapid growth of smart logistics, driven by IoT devices and data-intensive applications, necessitates secure, scalable, and efficient computing frameworks. As the edge-fog-cloud (EFC) paradigm supports real-time data processing, it faces significant security threats and attacks, including privacy risks, data breaches, and unauthorized access. To address these security threats and attacks, blockchain and federated learning (FL) have gained popularity as potential solutions in EFC computing environments for smart logistics. This survey reviews the current landscape in EFC computing environments for smart logistics, highlighting the existing benefits and challenges identified in 134 research studies published between January 2023 and June 2025. The applications of blockchain and FL demonstrate their ability to enhance data security and privacy, improve real-time tracking and monitoring, and ensure inventory and supply chain optimization. Although these technologies offer promising solutions, challenges such as scalability issues, data quality, interoperability and standardization hinder their effective implementation. The survey suggests future research directions focused on developing advanced blockchain and FL, integrating emerging technologies, developing policies and regulations, fostering collaborative research, and ensuring cross-industry adoption and interoperability. Integrating blockchain and FL within the EFC model offers a transformative path toward building secure, intelligent, and resilient logistics systems.
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- author
- Ali, Guma ; Thomas, Adebo ; Mijwil, Maad M. ; Al-Mahzoum, Kholoud ; Sallam, Malik LU ; Salau, Ayodeji Olalekan ; Adamopoulos, Ioannis ; Bala, Indu and Al-Jubori, Aseed Yaseen Rashid
- organization
- publishing date
- 2025-05-07
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Blockchain Technology, Data Privacy, Edge-Fog-Cloud Computing, Federated Learning, Smart Logistics
- in
- Mesopotamian Journal of CyberSecurity
- volume
- 5
- issue
- 2
- pages
- 35 pages
- publisher
- Mesopotamian Academic Press
- external identifiers
-
- scopus:105012200094
- ISSN
- 2958-6542
- DOI
- 10.58496/MJCS/2025/044
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025, Mesopotamian Academic Press. All rights reserved.
- id
- d81297f2-4120-4602-97ea-e24470560d3f
- date added to LUP
- 2025-12-29 14:47:00
- date last changed
- 2026-02-03 09:14:53
@article{d81297f2-4120-4602-97ea-e24470560d3f,
abstract = {{<p>The rapid growth of smart logistics, driven by IoT devices and data-intensive applications, necessitates secure, scalable, and efficient computing frameworks. As the edge-fog-cloud (EFC) paradigm supports real-time data processing, it faces significant security threats and attacks, including privacy risks, data breaches, and unauthorized access. To address these security threats and attacks, blockchain and federated learning (FL) have gained popularity as potential solutions in EFC computing environments for smart logistics. This survey reviews the current landscape in EFC computing environments for smart logistics, highlighting the existing benefits and challenges identified in 134 research studies published between January 2023 and June 2025. The applications of blockchain and FL demonstrate their ability to enhance data security and privacy, improve real-time tracking and monitoring, and ensure inventory and supply chain optimization. Although these technologies offer promising solutions, challenges such as scalability issues, data quality, interoperability and standardization hinder their effective implementation. The survey suggests future research directions focused on developing advanced blockchain and FL, integrating emerging technologies, developing policies and regulations, fostering collaborative research, and ensuring cross-industry adoption and interoperability. Integrating blockchain and FL within the EFC model offers a transformative path toward building secure, intelligent, and resilient logistics systems.</p>}},
author = {{Ali, Guma and Thomas, Adebo and Mijwil, Maad M. and Al-Mahzoum, Kholoud and Sallam, Malik and Salau, Ayodeji Olalekan and Adamopoulos, Ioannis and Bala, Indu and Al-Jubori, Aseed Yaseen Rashid}},
issn = {{2958-6542}},
keywords = {{Blockchain Technology; Data Privacy; Edge-Fog-Cloud Computing; Federated Learning; Smart Logistics}},
language = {{eng}},
month = {{05}},
number = {{2}},
pages = {{735--769}},
publisher = {{Mesopotamian Academic Press}},
series = {{Mesopotamian Journal of CyberSecurity}},
title = {{Blockchain and Federated Learning in Edge-Fog-Cloud Computing Environments for Smart Logistics}},
url = {{http://dx.doi.org/10.58496/MJCS/2025/044}},
doi = {{10.58496/MJCS/2025/044}},
volume = {{5}},
year = {{2025}},
}