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Computers
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Mobile Fog and Edge Computing
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Mobile Fog and Edge Computing

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 876

Special Issue Editor

Dr. Guangsheng Feng

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Engineering University, Harbin, China
Interests: network technology; information security; group intelligence system; intelligent security; wireless indoor positioning technology; internet of things; edge intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The interconnection of AI devices marks a pivotal event in the transition of society from the information age to the era of intelligence. Therefore, with the widespread adoption and application of an increasing number of intelligent devices, particularly mobile smart devices, traditional computational paradigms are encountering significant challenges in deployment, testing, and implementation. More flexible, data-proximate, and highly real-time computing architectures have become a focal point of joint interest in both academia and industry.

 Accordingly, this Special Issue focuses on mobile fog and edge computing, aiming to foster innovative discussions on corresponding topics to advance the development of computing paradigms adapted to the next generation of networks.

Topics of interest include, but are not limited to, the following:

  1. Design, testing, performance evaluation, and security analysis of next-generation mobile fog computing and edge computing paradigms;
  2. The training and deployment of artificial intelligence models based on novel computing paradigms;
  3. Design and analysis of network communication protocols in mobile computing scenarios;
  4. Optimization of novel applications and algorithmic research in mobile computing contexts;
  5. Design and analysis of next-generation Internet of Things (IoT) network protocols;
  6. Autonomous collaborative optimization and decision-making among multiple intelligent agents in mobile computing scenarios;
  7. 5G/6G-enabled mobile computing scenarios;
  8. End-to-end LLMs applications in mobile computing scenarios;
  9. 9. Multimodal large model applications for mobile computing scenarios.

Dr. Guangsheng Feng
Guest Editor

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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • mobile fog and edge computing
  • mobile AI paradigm
  • next-generation IoT
  • mobile autonomous collaborative
  • 5G/6G mobile computing

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Published Papers (1 paper)

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Research

26 pages, 5143 KB  
Article
Research on the Application of Federated Learning Based on CG-WGAN in Gout Staging Prediction
by Junbo Wang, Kaiqi Zhang, Zhibo Guan, Zi Ye, Chao Ma and Hai Huang
Computers 2025, 14(11), 455; https://doi.org/10.3390/computers14110455 - 23 Oct 2025
Viewed by 374
Abstract
Traditional federated learning frameworks face significant challenges posed by non-independent and identically distributed (non-IID) data in the healthcare domain, particularly in multi-institutional collaborative gout staging prediction. Differences in patient population characteristics, distributions of clinical indicators, and proportions of disease stages across hospitals lead [...] Read more.
Traditional federated learning frameworks face significant challenges posed by non-independent and identically distributed (non-IID) data in the healthcare domain, particularly in multi-institutional collaborative gout staging prediction. Differences in patient population characteristics, distributions of clinical indicators, and proportions of disease stages across hospitals lead to inefficient model training, increased category prediction bias, and heightened risks of privacy leakage. In the context of gout staging prediction, these issues result in decreased classification accuracy and recall, especially when dealing with minority classes. To address these challenges, this paper proposes FedCG-WGAN, a federated learning method based on conditional gradient penalization in Wasserstein GAN (CG-WGAN). By incorporating conditional information from gout staging labels and optimizing the gradient penalty mechanism, this method generates high-quality synthetic medical data, effectively mitigating the non-IID problem among clients. Building upon the synthetic data, a federated architecture is further introduced, which replaces traditional parameter aggregation with synthetic data sharing. This enables each client to design personalized prediction models tailored to their local data characteristics, thereby preserving the privacy of original data and avoiding the risk of information leakage caused by reverse engineering of model parameters. Experimental results on a real-world dataset comprising 51,127 medical records demonstrate that the proposed FedCG-WGAN significantly outperforms baseline models, achieving up to a 7.1% improvement in accuracy. Furthermore, by maintaining the composite quality score of the generated data between 0.85 and 0.88, the method achieves a favorable balance between privacy preservation and model utility. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing)
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