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Electronics
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Recent Advances in Collaborative Systems and Control in the Industrial...
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Recent Advances in Collaborative Systems and Control in the Industrial Sector

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

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 439

Special Issue Editors

Prof. Dr. Luis Pires

E-Mail Website
Guest Editor
Technologies Engineering School (EET), Lusofonia Polytechnic Institute (IPLuso), 1700-098 Lisbon, Portugal
Interests: electronics; IoT; WSSN; sensors; communications; robotics; automation; intelligent sensors; networks
Prof. Dr. Ricardo Santos

E-Mail Website
Guest Editor
1. Technologies Engineering School (EET), Lusofonia Polytechnic Institute (IPLuso), 1700-098 Lisbon, Portugal
2. GOVCOPP, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: intelligent systems; operational research; open innovation; sustainability; modelling; renewable energy applications; electrical vehicles and variable speed drives
Prof. Dr. Joaquim Monteiro

E-Mail Website
Guest Editor
INESC-ID, Instituto Superior de Engenharia de Lisboa (ISEL) of the Polytechnic Institute of Lisbon, 1959-007 Lisbon, Portugal
Interests: modelling; simulation and advanced control of power-electronic converters; control systems; renewable energy applications; electrical vehicles and variable speed drives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are thrilled to invite you to contribute to our upcoming publication on the latest advancements in Collaborative Systems and Control, as well as the exciting convergence of Internet of Things (IoT) and Artificial Intelligence (AI). This is an exceptional opportunity to showcase your expertise and share your pioneering research with a global audience.

This Special Issue will highlight the remarkable progress made in Collaborative Systems and Control, IoT, and AI, and the synergies emerging from their intersection. We invite you to explore the cutting-edge developments, trends, and breakthroughs within these domains, and demonstrate how they collectively shape the future of technology and research.

Potential topics include (but are not limited to):

  • Collaborative systems in open innovation ecosystems;
  • Intelligent Systems in industry;
  • Multi-agent coordination and collaboration;
  • Energy management systems and advanced control;
  • Smart control and operation of energy systems;
  • Decision-making applications in collaboration systems;
  • Internet of Things (IoT) and Artificial Intelligence (AI) applied to Control Systems;
  • Integration of AI algorithms in IoT ecosystems;
  • AI-driven data analysis for IoT-generated data;
  • Smart environments and IoT-enabled intelligent systems;
  • Predictive maintenance and anomaly detection using AI and IoT.

Submission Guidelines:

We welcome original research articles, reviews, and case studies that delve into the recent advances and challenges within Cooperative Systems and Control, IoT, and AI. Submissions will undergo a rigorous peer-review process prior to acceptance.

We look forward to receiving your contributions.

Prof. Dr. Luis Pires
Prof. Dr. Ricardo Santos
Prof. Dr. Joaquim Monteiro
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

  • collaborative systems
  • optimization techniques for control systems
  • Internet of Things (IoT)
  • smart environments
  • IoT integration
  • AI algorithms applied to control systems
  • predictive maintenance
  • intelligent systems
  • open innovation
  • advanced control

Published Papers (1 paper)

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Research

16 pages, 878 KiB  
Article
Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster Pairing
by Xiaobao Sha, Wenjian Sun, Xiang Liu, Yang Luo and Chunbo Luo
Electronics 2024, 13(11), 2135; https://doi.org/10.3390/electronics13112135 - 30 May 2024
Viewed by 61
Abstract
Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number of clients while preserving data privacy by keeping the data local. However, many existing FL frameworks have a two-layered [...] Read more.
Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number of clients while preserving data privacy by keeping the data local. However, many existing FL frameworks have a two-layered architecture, thus requiring the frequent exchange of large-scale model parameters between clients and remote cloud servers over often unstable networks and resulting in significant communication overhead and latency. To address this issue, we propose to introduce edge servers between the clients and the cloud server to assist in aggregating local models, thus combining asynchronous client–edge model aggregation with synchronous edge–cloud model aggregation. By leveraging the clients’ idle time to accelerate training, the proposed framework can achieve faster convergence and reduce the amount of communication traffic. To make full use of the grouping properties inherent in three-layer FL, we propose a similarity matching strategy between edges and clients, thus improving the effect of asynchronous training. We further propose to introduce model-contrastive learning into the loss function and personalize the clients’ local models to address the potential learning issues resulting from asynchronous local training in order to further improve the convergence speed. Extensive experiments confirm that our method exhibits significant improvements in model accuracy and convergence speed when compared with other state-of-the-art federated learning architectures. Full article
(This article belongs to the Special Issue Recent Advances in Collaborative Systems and Control in the Industrial Sector)
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