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Applied Sciences
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Research on Distributed Systems and Cloud Computing
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Research on Distributed Systems and Cloud Computing

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

Deadline for manuscript submissions: 25 May 2024 | Viewed by 1031

Special Issue Editor

Prof. Dr. Eui-Nam Huh

E-Mail Website
Guest Editor
Department of Computer Science & Engineering, College of Software, Kyung Hee University, Seoul 02447, Republic of Korea
Interests: cloud computing; the Internet of Things; future internet; distributed real-time systems; mobile computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cloud computing is accommodating more diverse resources, as many new computing architectures are produced especially for AI machine learning support. So, distributed cloud technology including edge nodes have emerged. Furthermore, IoT devices and industries’ own servers collect data on demand and provide computing power, respectively. Also, hyperscale AI services have been launched, requiring distributed heterogeneous resources for model training and model serving in a cloud computing environment. Therefore, new challenges are emerging to support hyperscale AI services using many different types of cloud continuum, such as edge-to-cloud, cloud-to-cloud (multi-cloud), IoT-to-edge, IoT-to-cloud ones, etc.

Prof. Dr. Eui-Nam Huh
Guest Editor

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

  • multi-cloud
  • continuum computing
  • AI as a service
  • data engineering
  • real-time systems
  • IoT as a service
  • energy
  • security

Published Papers (1 paper)

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Research

17 pages, 8713 KiB  
Article
Towards Client Selection in Satellite Federated Learning
by Changhao Wu, Siyang He, Zengshan Yin and Chongbin Guo
Appl. Sci. 2024, 14(3), 1286; https://doi.org/10.3390/app14031286 - 04 Feb 2024
Viewed by 803
Abstract
Large-scale low Earth orbit (LEO) remote satellite constellations have become a brand new, massive source of space data. Federated learning (FL) is considered a promising distributed machine learning technology that can communicate optimally using these data. However, when applying FL in satellite networks, [...] Read more.
Large-scale low Earth orbit (LEO) remote satellite constellations have become a brand new, massive source of space data. Federated learning (FL) is considered a promising distributed machine learning technology that can communicate optimally using these data. However, when applying FL in satellite networks, it is necessary to consider the unique challenges brought by satellite networks, which include satellite communication, computational ability, and the interaction relationship between clients and servers. This study focuses on the siting of parameter servers (PSs), whether terrestrial or extraterrestrial, and explores the challenges of implementing a satellite federated learning (SFL) algorithm equipped with client selection (CS). We proposed an index called “client affinity” to measure the contribution of the client to the global model, and a CS algorithm was designed in this way. A series of experiments have indicated the advantage of our SFL paradigm—that satellites function as the PS—and the availability of our CS algorithm. Our method can halve the convergence time of both FedSat and FedSpace, and improve the precision of the models by up to 80%. Full article
(This article belongs to the Special Issue Research on Distributed Systems and Cloud Computing)
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