Trends in Information Systems and Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 296

Special Issue Editor

Special Issue Information

Dear Colleagues,

We invite researchers, practitioners, and industry experts to submit their original research and innovative solutions for this Special Issue titled "Trends in Information Systems and Security". This Special Issue aims to innovative research in the field of information systems and security.  This Special Issue seeks to explore the latest advancements, emerging challenges, and opportunities within the dynamic field of information systems and security. Potential topics include but are not limited to the following research areas in information systems and security:

  • Artificial intelligence;
  • Machine learning;
  • Blockchain;
  • Internet of Things;
  • Cloud computing;
  • Big data analytics.

Dr. Namgi Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • information system
  • information security
  • information network
  • cyber security
  • internet of things

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Published Papers (1 paper)

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Research

12 pages, 667 KiB  
Article
Non-IID Degree Aware Adaptive Federated Learning Procedure Selection Scheme for Edge-Enabled IoT Network
by Sanghui Lee and Jaewook Lee
Electronics 2025, 14(12), 2331; https://doi.org/10.3390/electronics14122331 - 7 Jun 2025
Viewed by 142
Abstract
Due to the independent, identically distributed (non-IID) nature of IoT device data, the traditional federated learning (FL) procedure, where IoT devices train the deep model in parallel, suffers from a degradation in learning accuracy. To mitigate this problem, a sequential FL procedure has [...] Read more.
Due to the independent, identically distributed (non-IID) nature of IoT device data, the traditional federated learning (FL) procedure, where IoT devices train the deep model in parallel, suffers from a degradation in learning accuracy. To mitigate this problem, a sequential FL procedure has been proposed, in which IoT devices train the deep model in a serialized manner via a parameter server. However, this approach experiences a longer convergence time due to the lack of parallelism. In this paper, we propose an adaptive FL procedure selection (AFLS) scheme that selects an appropriate FL scheme, either the traditional or the sequential FL procedures, based on the degree of non-IID among IoT devices to achieve both the required learning accuracy and low convergence time. To further reduce the convergence time of the sequential FL procedure, we also introduce a device-to-device (D2D)-based sequential FL procedure. The evaluation results demonstrate that AFLS can reduce convergence time by up to 16% compared to the sequential FL procedure and improve learning accuracy by up to 6∼26% compared to the traditional FL procedure. Full article
(This article belongs to the Special Issue Trends in Information Systems and Security)
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