Advances in Data-Driven Distributed Intelligence for Network Efficiency, Security, Measurement and Trust
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".
Deadline for manuscript submissions: 15 June 2024 | Viewed by 130
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue titled "Advances in Data-Driven Distributed Intelligence for Network Efficiency, Security, Measurement and Trust" in the MDPI Electronics journal is dedicated to the application of data-driven methods and distributed AI techniques, including Federated Learning, Gossip Learning, and Split Learning, in the domains of the Efficiency, Security, Measurement, and Trust in computer networking. These techniques hold particular significance when addressing the complex challenges encompassed by ad hoc networks and IoT networks, which operate within dynamic, energy-sensitive environments. By integrating distributed AI methodologies, this Special Issue aims to bolster network security, streamline authentication processes, refine measurement accuracy, and enhance trust, all while safeguarding users’ privacy and optimizing the energy resources. This Special Issue seeks to uncover innovative approaches for harnessing distributed AI's potential for Efficiency, Measurement, Security, and Trust, especially within dynamic network settings and energy-efficient IoT environments. By emphasizing the practical implementation of these advanced technologies, this endeavor contributes to a more secure, privacy-focused, and energy-efficient future in computer networking.
The topics of interest include, but are not limited, to the following:
- Data-Driven Methods;
- Distributed Machine Learning;
- Federated Learning;
- Gossip Learning;
- Split Learning;
- (Mobile) Ad Hoc Networking;
- IoT (Internet of Things);
- Performance Evaluation;
- Energy Efficiency;
- Measurement.
Dr. Antonio Di Maio
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. 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
- data-driven methods
- distributed machine learning
- federated learning
- gossip learning
- split learning
- (mobile) ad hoc networking
- IoT (internet of things)
- performance evaluation
- energy efficiency
- measurement
- trust
