Machine Learning for Edge Computing
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 128
Special Issue Editors
Interests: artificial intelligence; edge computing; connected autonomous vehicles; LLM; cybersecurity
Interests: connected and autonomous vehicles; edge and cloud computing; cyberinfrastructures; cybersecurity; distributed and IoT systems; intelligent systems; machine learning; high performance computing
Special Issue Information
Dear Colleagues,
We are delighted to invite you to submit your latest research to this Special Issue titled "Machine Learning for Edge Computing". This integration marks a pivotal shift, bringing computational intelligence closer to data sources, significantly reducing latency, and enhancing privacy. We aim to explore innovative research and advancements in deploying machine learning algorithms directly on edge devices, which face stringent power and computational constraints.
These devices present unique challenges that demand efficient and robust ML solutions, capable of operating under limited resources while being crucial for rapid data processing and decision-making. We seek high-quality papers addressing both theoretical advancements in algorithm design and practical implementations that showcase novel algorithmic adaptations and system designs. These should enable sophisticated ML tasks to be performed efficiently on edge devices.
Potential topics include, but are not limited to, lightweight neural networks, federated learning, real-time data processing, energy-efficient ML architectures, and ML applications at the edge. We particularly welcome submissions demonstrating innovative approaches to adapting algorithms for reduced power consumption, efficient computation, and the trade-offs between computational complexity and performance in edge scenarios.
Contributions may range from exploring the balance between accuracy and computational demand in applications such as connected vehicles, smart cities, IoT systems, and the edge-cloud continuum to investigating the impact of machine learning on the privacy and security of edge computing systems. This Special Issue provides a platform for researchers and practitioners from academia and industry to share their insights and findings, hel** us to push the boundaries of what is possible in edge computing with machine learning.
We invite you to contribute to this cutting-edge discussion by submitting your research, reviews, or communication articles to this timely Special Issue.
Dr. Sihai Tang
Prof. Dr. Song Fu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at mdpi.longhoe.net 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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
- federated learning
- real-time data processing
- adaptive algorithms
- energy-efficient machine learning
- privacy and security in edge computing
- autonomous decision making
