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The New Age of Edge Intelligence and Its Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 2029

Special Issue Editors


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Guest Editor
College of Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: wireless networks; LTE; 5G; Internet of Things; mobile computing

E-Mail Website
Guest Editor
School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: edge computing; federated learning; intelligent communications; C-RAN; massive MIMO

Special Issue Information

Dear Colleagues,

With the rapid development of mobile networks and the Internet of Things, the number of devices, as well as the amount of data generated by these devices, at the edge of networks is rapidly increasing. This trend has prompted a new computing paradigm: edge computing. At the same time, thanks to breakthroughs in computing hardware and machine learning algorithms, edge computing acts as a smart “brain” and provides more intelligent services. Edge intelligence can be roughly divided into two types, i.e., AI on edge and AI for edge, where the former focuses on how to build AI models on the edge computing platform and the latter focuses on how to provide solutions to edge computing problems using advanced AI techniques. In this context, this Special Issue aims to present a collection of work on the theme of edge intelligence and its applications.

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

  • Intelligent solutions to edge computing problems, e.g., task offloading and edge caching, and resource allocation;
  • Intelligent applications at the edge;
  • Distributed learning and federated learning;
  • Edge–cloud collaborative learning;
  • Learning-based security and privacy;
  • Optimization techniques in edge computing;
  • Low-latency and energy-aware processing;
  • Fault tolerance, reliability, and survivability in edge computing;
  • AI lightweight models and model compression.

Dr. Yunjian Jia
Dr. Wenchao Xia
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. Applied Sciences 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

  • edge intelligence
  • edge computing
  • intelligent communications
  • distributed learning
  • cloud-edge collaboration
  • task offloading
  • edge caching
  • resource allocation

Published Papers (2 papers)

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Research

12 pages, 518 KiB  
Article
Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks
by Jie Zhao, Yiyang Ni and Yulun Cheng
Appl. Sci. 2024, 14(2), 542; https://doi.org/10.3390/app14020542 - 8 Jan 2024
Viewed by 854
Abstract
Federated learning (FL) is a promising technique to provide intelligent services for the internet of things (IoT). By transmitting the model parameters instead of user data between the client and central server, FL greatly improves the user privacy and reduces transmission latency. However, [...] Read more.
Federated learning (FL) is a promising technique to provide intelligent services for the internet of things (IoT). By transmitting the model parameters instead of user data between the client and central server, FL greatly improves the user privacy and reduces transmission latency. However, due to the fading effects of the wireless channel, the outage of wireless transmission degenerates the learning efficiency when FL is applied in wireless IoT networks. In order to address this issue, we investigate the joint optimization of client selection and wireless resource allocation in FL-aided cellular IoT networks. By taking both the amount of training data and wireless resource consumption into consideration, we formulate the problem as a mixed integer non-linear programming to maximize the utility of the network. To solve the problem effectively, an alternative direction-based algorithm is proposed by decomposing the original problem into two sub problems. The simulation results indicate that the proposed algorithm substantially improves the FL learning performance and reduces the consumption of wireless resources compared with existing methods. Full article
(This article belongs to the Special Issue The New Age of Edge Intelligence and Its Applications)
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13 pages, 936 KiB  
Article
Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
by Jian Yang, Yan Zhou, Wanli Wen, Jin Zhou and Qingrui Zhang
Appl. Sci. 2023, 13(20), 11134; https://doi.org/10.3390/app132011134 - 10 Oct 2023
Cited by 2 | Viewed by 801
Abstract
Federated learning (FL) offers a promising solution in edge computing to overcome bandwidth limitations and privacy concerns associated with traditional cloud-based training. However, current FL methods often suffer from transmission delay and excessive communication resource usage. In this paper, we introduce an innovative [...] Read more.
Federated learning (FL) offers a promising solution in edge computing to overcome bandwidth limitations and privacy concerns associated with traditional cloud-based training. However, current FL methods often suffer from transmission delay and excessive communication resource usage. In this paper, we introduce an innovative asynchronous hierarchical FL approach based on bandwidth allocation and client scheduling. Specifically, we propose an efficient algorithm that dynamically assigns clients to edge servers based on client mobility during training and accelerates parameter uploading while taking into account the remaining bandwidth of the edge servers. Our experimental results demonstrate the effectiveness of our approach, particularly in scenarios with frequent client mobility. This research strongly supports the application of FL in edge computing and underscores the crucial role of resource allocation in addressing communication resource constraints and reducing the training time of FL. Full article
(This article belongs to the Special Issue The New Age of Edge Intelligence and Its Applications)
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