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Electronics
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AI for Edge Computing
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AI for Edge Computing

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 3175

Special Issue Editors

Dr. Jimmy Ming-Tai Wu

E-Mail Website
Guest Editor
Department of Information Management, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan
Interests: data mining; big data; artificial intelligence
Dr. Matin Pirouz

E-Mail Website
Guest Editor
College of Science and Mathematics, California State University, Fresno, CA 93740, USA
Interests: big data; data analytics; complex network analysis
Dr. Shahab Tayeb

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, California State University, Fresno, CA 93740, USA
Interests: network security; cybersecurity; security and privacy; Internet of Things; internet of vehicles

Special Issue Information

Dear Colleagues,

The focus of this Special Issue proposal is on AI for edge computing, because “letting AI enhance the performance of edge computing” and “letting AI run on edge” are two promising research topics nowadays, especially in modern information systems, such as the traffic light control system and other relevant smart city applications. Among the issues, downsizing machine learning, data mining, deep learning to make them run on the edge, finding a set of suitable hyperparameters, and even searching for a good neural architecture for the deep neural network have attracted the attention of researchers from different disciplines. Different from using AI technologies to realize intelligent systems/applications, these research directions can be regarded as an essential part of studies on AI in the forthcoming future, especially for ICT and smart cities. Therefore, we can foresee that the integration of artificial intelligent methods for edge computing will become a popular research topic. That is why this Special Issue will be focusing on intelligent algorithms for edge computing and their applications, such as downsizing and accelerating solutions of AI and edge devices/servers.

It is expected that this Special Issue will attract a lot of submissions from the research societies of AI, the Internet of Things (IoT), Wireless Sensor Network (WSN), smart city, and so forth. We can then select some high-quality papers from them to further increase the reputation and impact of ICT Express.

Dr. Jimmy Ming-Tai Wu
Dr. Matin Pirouz
Dr. Shahab Tayeb
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

  • edge computing
  • machine learning
  • Internet of Things (IoT)
  • artificial intelligence
  • ICT (information and communication technology)

Published Papers (3 papers)

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Research

20 pages, 2703 KiB  
Article
One-Shot Federated Learning with Label Differential Privacy
by Zikang Chen, Changli Zhou and Zhenyu Jiang
Electronics 2024, 13(10), 1815; https://doi.org/10.3390/electronics13101815 - 8 May 2024
Viewed by 279
Abstract
Federated learning (FL) has emerged as an extremely effective strategy for dismantling data silos and has attracted significant interest from both industry and academia in recent years. However, existing iterative FL approaches often require a large number of communication rounds and struggle to [...] Read more.
Federated learning (FL) has emerged as an extremely effective strategy for dismantling data silos and has attracted significant interest from both industry and academia in recent years. However, existing iterative FL approaches often require a large number of communication rounds and struggle to perform well on unbalanced datasets. Furthermore, the increased complexity of networks makes the application of traditional differential privacy to protect client privacy expensive. In this context, the authors introduce FedGM: a method designed to reduce communication overhead and achieve outstanding results in non-IID scenarios. FedGM is capable of achieving considerable accuracy, even with a small privacy budget. Specifically, the authors devise a method to extract knowledge from each client’s data by creating a scaled-down but highly effective synthesized dataset that can perform similarly to the original data. Additionally, the authors propose an innovative approach to applying label differential privacy to protect the synthesized dataset. The authors demonstrate the superiority of the approach over traditional methods by requiring only one communication round and by testing using four classification datasets for evaluation. Furthermore, when comparing the model performance for clients using their method against traditional solutions, the authors find that the approach achieves significant accuracy and better privacy. Full article
(This article belongs to the Special Issue AI for Edge Computing)
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16 pages, 9544 KiB  
Article
Personalized Federated Learning Incorporating Adaptive Model Pruning at the Edge
by Yueying Zhou, Gaoxiang Duan, Tianchen Qiu, Lin Zhang, Li Tian, Xiaoying Zheng and Yongxin Zhu
Electronics 2024, 13(9), 1738; https://doi.org/10.3390/electronics13091738 - 1 May 2024
Viewed by 434
Abstract
Edge devices employing federated learning encounter several obstacles, including (1) the non-independent and identically distributed (Non-IID) nature of client data, (2) limitations due to communication bottlenecks, and (3) constraints on computational resources. To surmount the Non-IID data challenge, personalized federated learning has been [...] Read more.
Edge devices employing federated learning encounter several obstacles, including (1) the non-independent and identically distributed (Non-IID) nature of client data, (2) limitations due to communication bottlenecks, and (3) constraints on computational resources. To surmount the Non-IID data challenge, personalized federated learning has been introduced, which involves training tailored networks at the edge; nevertheless, these methods often exhibit inconsistency in performance. In response to these concerns, a novel framework for personalized federated learning that incorporates adaptive pruning of edge-side data is proposed in this paper. This approach, through a two-staged pruning process, creates customized models while ensuring strong generalization capabilities. Concurrently, by utilizing sparse models, it significantly condenses the model parameters, markedly diminishing both the computational burden and communication overhead on edge nodes. This method achieves a remarkable compression ratio of 3.7% on the Non-IID dataset FEMNIST, with the training accuracy remaining nearly unaffected. Furthermore, the total training duration is reduced by 46.4% when compared with the standard baseline method. Full article
(This article belongs to the Special Issue AI for Edge Computing)
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18 pages, 686 KiB  
Article
Computational Offloading for MEC Networks with Energy Harvesting: A Hierarchical Multi-Agent Reinforcement Learning Approach
by Yu Sun and Qijie He
Electronics 2023, 12(6), 1304; https://doi.org/10.3390/electronics12061304 - 9 Mar 2023
Cited by 5 | Viewed by 1882
Abstract
Multi-access edge computing (MEC) is a novel computing paradigm that leverages nearby MEC servers to augment the computational capabilities of users with limited computational resources. In this paper, we investigate the computational offloading problem in multi-user multi-server MEC systems with energy harvesting, aiming [...] Read more.
Multi-access edge computing (MEC) is a novel computing paradigm that leverages nearby MEC servers to augment the computational capabilities of users with limited computational resources. In this paper, we investigate the computational offloading problem in multi-user multi-server MEC systems with energy harvesting, aiming to minimize both system latency and energy consumption by optimizing task offload location selection and task offload ratio.We propose a hierarchical computational offloading strategy based on multi-agent reinforcement learning (MARL). The proposed strategy decomposes the computational offloading problem into two sub-problems: a high-level task offloading location selection problem and a low-level task offloading ratio problem. The complexity of the problem is reduced by decoupling. To address these sub-problems, we propose a computational offloading framework based on multi-agent proximal policy optimization (MAPPO), where each agent generates actions based on its observed private state to avoid the problem of action space explosion due to the increasing number of user devices. Simulation results show that the proposed HDMAPPO strategy outperforms other baseline algorithms in terms of average task latency, energy consumption, and discard rate. Full article
(This article belongs to the Special Issue AI for Edge Computing)
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