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Data-Driven Decentralized Learning for Future Communication Networks
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Data-Driven Decentralized Learning for Future Communication Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1995

Special Issue Editors

Dr. Chuanting Zhang

E-Mail Website
Guest Editor
School of Software, Shandong University, Jinan 250012, China
Interests: federated learning; time series analysis; spatial-temporal data analysis
Dr. Shuping Dang

E-Mail Website
Guest Editor
School of Electrical, Electronic and Mechanical Engineering, University of Bristol, Bristol BS8 1UB, UK
Interests: communication networks; artificial intelligence; 6G
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, academia and industry have witnessed the prosperity and development of artificial intelligence technology, and, more noticeably, various intelligent applications are increasingly sinking to the edge close to users. In this regard, data-driven decentralized learning technology based on cloud–edge collaboration has received widespread attention. Decentralized learning in the context of cloud–edge collaboration is a new architecture combining cloud computing and edge computing with the help of networks. It utilizes the mighty computing power and storage capacity of cloud computing, along with the low latency, high reliability, and flexibility of edge computing to achieve better computing performance and user experience. This collaboration processes data and tasks jointly between the cloud and the edge. Edge devices can collect and process data through sensors and other devices and then delegate some tasks requiring more substantial computing power to the cloud for processing. Simultaneously, the cloud can reduce its workload and improve response speed by delegating some data and tasks to edge devices. This technology can enhance computing efficiency and reliability, improving users' experience. It has become the key to supporting the realization of 6G edge intelligence. Cloud–edge collaboration not only offers strong support for edge networking, resource allocation, and network optimization (AI for edge), it also provides computing services and collaborative intelligence and reduces latency to meet the real-time business needs of the network (AI on edge).

This Special Issue aims to collect new innovative ideas to apply data-driven decentralized learning models and algorithms for future communication networks. Both primarily theoretical deduction and applied decentralized learning technologies based on mathematical ideas are welcomed in this Special Issue.

Dr. Chuanting Zhang
Dr. Shuping Dang
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. Mathematics 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 2600 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

  • decentralized learning for wireless communications
  • cloud–edge–deivce collaborative learning
  • wireless traffic analysis and resource management
  • intelligent communications
  • large language models and their application in communication networks

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Published Papers (2 papers)

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17 pages, 11684 KiB  
Article
Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI
by Anming Dong, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun and Jiguo Yu
Mathematics 2025, 13(8), 1227; https://doi.org/10.3390/math13081227 - 9 Apr 2025
Viewed by 294
Abstract
Abnormal gait recognition, which aims to detect and identify deviations from normal walking patterns indicative of various health conditions or impairments, holds promising applications in healthcare and many other related fields. Currently, Wi-Fi-based abnormal gait recognition methods in the literature mainly distinguish the [...] Read more.
Abnormal gait recognition, which aims to detect and identify deviations from normal walking patterns indicative of various health conditions or impairments, holds promising applications in healthcare and many other related fields. Currently, Wi-Fi-based abnormal gait recognition methods in the literature mainly distinguish the normal and abnormal gaits, which belongs to coarse-grained classification. In this work, we explore fine-grained gait rectification methods for distinguishing multiple classes of abnormal gaits. Specifically, we propose a deep learning-based framework for multi-class abnormal gait recognition, comprising three key modules: data collection, data preprocessing, and gait classification. For the gait classification module, we design a hybrid deep learning architecture that integrates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), and an attention mechanism to enhance performance. Compared to traditional CNNs, which rely solely on spatial features, or recurrent neural networks like long short-term memory (LSTM) and gated recurrent units (GRUs), which primarily capture temporal dependencies, the proposed CNN-BiGRU network integrates both spatial and temporal features concurrently. This dual-feature extraction capability positions the proposed CNN-BiGRU architecture as a promising approach for enhancing classification accuracy in scenarios involving multiple gaits with subtle differences in their characteristics. Moreover, the attention mechanism is employed to selectively focus on critical spatiotemporal features for fine-grained abnormal gait detection, enhancing the model’s sensitivity to subtle anomalies. We construct an abnormal gait dataset comprising seven distinct gait classes to train and evaluate the proposed network. Experimental results demonstrate that the proposed method achieves an average recognition accuracy of 95%, surpassing classical baseline models by at least 2%. Full article
(This article belongs to the Special Issue Data-Driven Decentralized Learning for Future Communication Networks)
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17 pages, 1464 KiB  
Article
STAR-RIS-Assisted Millimeter-Wave Secure Communication with Multiple Eavesdroppers
by Binghui Qian, Jingping Qiao and Chuanting Zhang
Mathematics 2024, 12(14), 2259; https://doi.org/10.3390/math12142259 - 19 Jul 2024
Viewed by 1097
Abstract
Aiming to address the limited coverage of conventional reconfigurable intelligent surfaces (RISs), this study proposes a millimeter-wave secure communication scheme based on the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). It uses the transmission and reflection functions of the STAR-RIS to achieve [...] Read more.
Aiming to address the limited coverage of conventional reconfigurable intelligent surfaces (RISs), this study proposes a millimeter-wave secure communication scheme based on the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). It uses the transmission and reflection functions of the STAR-RIS to achieve multiple users’ full-area communication coverage and meet the security communication needs of different users. To maximize the system sum rate under the security communication requirements of users in the transmission and reflection regions, this study proposes a joint optimization design scheme consisting of transmit beamforming at the base station (BS) and transmitting and reflecting coefficients at the STAR-RIS based on the energy-splitting protocol, and it models the rate optimization problem with information leakage constraints under imperfect eavesdroppers’ channel state information (ECSI). First, a series of transformations is proposed to solve the coupling between the optimization variables, and then, an efficient iterative algorithm based on successive convex approximation (SCA) and semi-definite relaxation (SDR) is proposed. Aiming to address the amplitude and phase constraints of the STAR-RIS, an optimization method comprising a penalty concave–convex procedure is adopted. The simulation results show that, compared with the conventional RIS, the proposed STAR-RIS assistance scheme can achieve the full coverage of the communication system and effectively improve the system sum rate while ensuring the safe transmission of information. The combination of STAR-RIS and millimeter-wave can promote the efficient and safe transmission of information in dense cities. Full article
(This article belongs to the Special Issue Data-Driven Decentralized Learning for Future Communication Networks)
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