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Electronics
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Advances in Intelligent Networks and Systems
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Advances in Intelligent Networks and Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 2037

Special Issue Editors

Dr. Gongming Zhao

E-Mail Website
Guest Editor
School of Computer Science and Technology, University of Science and Technology of China, Hefei 215028, China
Interests: cloud computing; software-defined networking; network function virtualization and data center networks
Dr. Yang Du

E-Mail Website
Guest Editor
School of Computer Science and Technology, Soochow University, Suzhou 215006, China
Interests: traffic measurement; software-defined networking
Dr. Yaxiong Xie

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY 14260, USA
Interests: next-generation networks; mobile computing; AIoT

Special Issue Information

Dear Colleagues,

Model training on sensing data is an important way to achieve the integration of the Internet of Things and artificial intelligence. Traditionally, sending massive amounts of sensing data to the cloud platform for training will result in high bandwidth consumption and possible privacy leakage, which is not suitable for various applications with bandwidth constraints and high privacy requirements. By contrast, using emerging networks, computing, storage, and other functions, edge computing provides model training services within a nearby range and empowers the Internet of Things.

This Special Issue aims to bring together researchers from academia and industry and present the latest research results in areas such as cloud computing, edge computing, cloud-edge synergy, 5G, data center networks and software-defined networks. We encourage prospective authors to submit related distinguished research papers on the subject of both theoretical approaches and practical case reviews.

Dr. Gongming Zhao
Dr. Yang Du
Dr. Yaxiong Xie
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. Electronics 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

  • intelligent networks
  • edge computing
  • cloud computing
  • federated learning
  • edge-cloud networks

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Published Papers (1 paper)

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Research

16 pages, 2632 KiB  
Article
Federated Learning Based on Mutual Information Clustering for Wireless Traffic Prediction
by Jianwei Zhang, Xinhua Hu, Zengyu Cai, Liang Zhu and Yuan Feng
Electronics 2023, 12(21), 4476; https://doi.org/10.3390/electronics12214476 - 31 Oct 2023
Cited by 4 | Viewed by 1608
Abstract
Wireless traffic prediction can help operators accurately predict the usage of wireless networks, and it plays an important role in the load balancing and energy saving of base stations. Currently, most traffic prediction methods are centralized learning strategies, which need to transmit a [...] Read more.
Wireless traffic prediction can help operators accurately predict the usage of wireless networks, and it plays an important role in the load balancing and energy saving of base stations. Currently, most traffic prediction methods are centralized learning strategies, which need to transmit a large amount of traffic data and have timeliness and data privacy issues. Federated learning, as a distributed learning framework with no client data sharing and multi-client collaborative training, can solve such problems. We propose a federated learning wireless traffic prediction framework based on mutual information clustering (FedMIC). First, a sliding window scheme is used to construct the raw data into adjacent and periodic dual-traffic sequences and capture their traffic characteristics separately to enhance the client model learning capability. Second, clients with similar traffic data distributions are clustered together using a mutual information-based spectral clustering algorithm to facilitate the capture of the personalized features of each clustered model. Then, models are aggregated using a hierarchical aggregation architecture of intra-cluster model aggregation and inter-cluster model aggregation to address the statistical heterogeneity challenge of federated learning and to improve the prediction accuracy of models. Finally, an attention mechanism-based model aggregation algorithm is used to improve the generalization ability of the global model. Experimental results show that our proposed method minimizes the prediction error and has superior traffic prediction performance compared to traditional distributed machine learning methods and other federated learning methods. Full article
(This article belongs to the Special Issue Advances in Intelligent Networks and Systems)
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