Edge Intelligence: Edge Computing for 5G and the Internet of Things, 2nd Edition

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 464

Special Issue Editors


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Guest Editor
Department of Computer Science and Technology, Tsinghua University, Beijing 100190, China
Interests: mobile computing; data privacy; machine learning (artificial intelligence); internet of things
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 10006, China
Interests: edge computing; edge intelligence; 5G/6G; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To enhance 5G and IoT networks with AI capabilities, large volumes of multi-modal sensing data (e.g., audio and video) are continuously generated by mobile and IoT devices located at the network edge. Driven by this trend, there is an urgent need to advance AI at the network edge to fully unlock the potential utilization of 5G and IoT networks for various smart services and applications. Thus, edge intelligence, an emerging paradigm that pushes AI tasks and services from the network core to the network edge, is increasingly recognized as a crucial component of next-generation intelligent networking systems. Research on edge intelligence is still in its preliminary stages; thus, a dedicated platform for the discussion, promotion, and dissemination of research in this field is highly desired by the networking, computing, and AI communities. To address this gap, this Special Issue aims to gather recent advances and novel contributions from academic researchers and industry practitioners in the areas of edge intelligence and edge computing for 5G and IoT networks. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Intelligent edge computing resource management for 5G/B5G/6G and IoT;
  • Edge computing system and AI model co-design for 5G/B5G/6G and IoT;
  • Cloud–edge–device converged computing for AI for 5G/B5G/6G and IoT;
  • Federated edge learning over for 5G/B5G/6G and IoT;
  • Distributed edge intelligence model training and inference;
  • Privacy-preserving methods for edge intelligence;
  • Large edge models and their applications for 5G/B5G/6G and IoT;
  • Distributed edge data analytics for 5G/B5G/6G and IoT;
  • Other emerging edge computing and edge intelligence techniques and applications for 5G/B5G/6G and IoT.

Dr. Yuezhi Zhou
Prof. Dr. Xu Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • edge computing
  • edge intelligence
  • edge large models
  • 5G/B5G/6G networks
  • IoT networks
  • federated edge learning
  • cloud-edge-device converged computing
  • distributed edge intelligence model training and inference
  • privacy-preserving edge intelligence

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Published Papers (1 paper)

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Research

18 pages, 546 KiB  
Article
Resource Allocation for Federated Learning with Heterogeneous Computing Capability in Cloud–Edge–Client IoT Architecture
by Xubo Zhang and Yang Luo
Future Internet 2025, 17(6), 243; https://doi.org/10.3390/fi17060243 - 30 May 2025
Viewed by 279
Abstract
A federated learning (FL) framework for cloud–edge–client collaboration performs local aggregation of model parameters through edges, reducing communication overhead from clients to the cloud. This framework is particularly suitable for Internet of Things (IoT)-based secure computing scenarios that require extensive computation and frequent [...] Read more.
A federated learning (FL) framework for cloud–edge–client collaboration performs local aggregation of model parameters through edges, reducing communication overhead from clients to the cloud. This framework is particularly suitable for Internet of Things (IoT)-based secure computing scenarios that require extensive computation and frequent parameter updates, as it leverages the distributed nature of IoT devices to enhance data privacy and reduce latency. To address the issue of high-computation-capability clients waiting due to varying computing capabilities under heterogeneous device conditions, this paper proposes an improved resource allocation scheme based on a three-layer FL framework. This scheme optimizes the communication parameter volume from clients to the edge by implementing a method based on random dropout and parameter completion before and after communication, ensuring that local models can be transmitted to the edge simultaneously, regardless of different computation times. This scheme effectively resolves the problem of high-computation-capability clients experiencing long waiting times. Additionally, it optimizes the similarity pairing method, the Shapley Value (SV) aggregation strategy, and the client selection method to better accommodate heterogeneous computing capabilities found in IoT environments. Experiments demonstrate that this improved scheme is more suitable for heterogeneous IoT client scenarios, reducing system latency and energy consumption while enhancing model performance. Full article
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