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Advances in Cloud/Edge Computing: AI, Application, Blockchain and Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 1065

Special Issue Editors


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Guest Editor
Department of Software Science, Dankook University, Yongin 16891, Republic of Korea
Interests: cloud computing; cloud architectures; sensor development

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Guest Editor
Department of Computer Engineering, Hongik University, 94 Mapo-gu, Wausan-ro, Seoul 04068, Republic of Korea
Interests: artificial intelligence technology in network security; medical welfare; smart city; Internet of Things

Special Issue Information

Dear Colleagues,

Cloud/Edge computing is evolving to accommodate new services/applications and technologies aligned with AI and blockchain. With AI services that offer various capabilities, and blockchain ensuring data integrity, these cutting-edge technologies each demand significant computing power. To meet these requirements, Cloud/Edge computing plays an important role, along with security considerations.

This Special Issue aims to facilitate the exchange of ideas and the sharing of the latest research results among researchers from various fields. We are pleased to invite you to submit your work to this Special Issue.

In this Special Issue, original research articles and reviews are welcome for submission. Research areas may include (but are not limited to) the following: 

  1. Cloud/edge computing;
  2. Cloud/edge applications;
  3. Distributed applications;
  4. Smart contracts;
  5. Security;
  6. AI as a service;
  7. Blockchain as a service. We look forward to receiving your contributions

Dr. Youngbeom Park
Dr. Young Yoon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cloud/edge computing
  • cloud/edge applications
  • distributed applications
  • smart contracts
  • security
  • AI as a service
  • blockchain as a service

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Published Papers (1 paper)

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Research

22 pages, 1740 KiB  
Article
CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
by Chongzhen Zhang, Hongye Sun, Zhaoyu Shen and Dongyu Wang
Appl. Sci. 2025, 15(1), 26; https://doi.org/10.3390/app15010026 - 24 Dec 2024
Viewed by 477
Abstract
Federated learning enables multiple intelligent devices to collaboratively perform machine learning tasks while preserving local data privacy. However, traditional FL architectures face challenges such as centralization and lack of effective defense mechanisms against malicious nodes, particularly in large-scale edge computing scenarios, which can [...] Read more.
Federated learning enables multiple intelligent devices to collaboratively perform machine learning tasks while preserving local data privacy. However, traditional FL architectures face challenges such as centralization and lack of effective defense mechanisms against malicious nodes, particularly in large-scale edge computing scenarios, which can lead to system instability. To address these challenges, this paper proposes a cross-zone secure federated learning method with blockchain and credibility mechanism, named CS-FL. By constructing a dual-layer blockchain network and introducing a blockchain ledger between zone servers, CS-FL establishes a decentralized trust mechanism for index detection and model aggregation. In node selection, CS-FL considers multiple dimensions, including node quality, communication resources, and historical credibility, and employs a three-stage mechanism that introduces lightweight probe tasks to assess node status before formal FL training, ensuring high-quality nodes participate. Additionally, the credibility incentive mechanism penalizes nodes that bypass probe mechanism and engage in malicious behaviors, effectively mitigating the impact of deceptive attacks. Experimental results show that CS-FL significantly improves the defense performance of FL, reducing attack success rates from 75–85% to below 5–20% when facing different types of threats, and effectively maintaining the training accuracy of the FL model. This demonstrates the potential of CS-FL to enhance the security and stability of FL systems in complex edge computing scenarios. Full article
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