Machine Learning in Networking Systems and Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".
Deadline for manuscript submissions: 15 October 2025 | Viewed by 213
Special Issue Editors
Interests: machine learning; smart grid; optimization; wireless communication
Special Issue Information
Dear Colleagues,
This Special Issue of Electronics, entitled "Machine Learning in Networking Systems and Applications", aims to delve into the dynamic interplay between machine learning (ML) and networking systems. The inherently complex nature of networking environments, characterized by multiple layers, numerous protocols, and extensive data interactions, makes them particularly amenable to ML applications. Machine learning introduces powerful data-driven methods to these intricate systems, significantly enhancing their efficiency and ability to manage the vast amounts of data they process daily.
Additionally, the role of networking systems is fundamental in enabling the functionality of ML. These systems are crucial for the vast data flows necessary for training and running sophisticated ML models, supporting distributed computational tasks, and facilitating multi-agent systems where multiple learning agents interact and collaborate. This symbiotic relationship is especially critical for deploying large-scale, resource-intensive ML models, such as those used in high-demand applications like ChatGPT, which require robust, scalable network solutions to handle significant increases in data traffic and computational demands.
In this Special Issue, we invite research that addresses how ML can enhance networking operations, how networks can support the functionality and effectiveness of ML technologies, or both. Contributors are encouraged to focus on any single aspect of this interaction, presenting innovative research or practical applications that demonstrate the enhancement of ML integrated with networking systems. Our goal is to showcase impactful research that addresses the challenges and solutions at the intersection of ML and networking.
Dr. Peizhong Ju
Dr. Chengzhang Li
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning (ML)
- networking systems
- federated learning
- multi-agent reinforcement learning
- ML at the edge
- scalable ML models
- distributed learning/intelligence
- data traffic management
- network-supported ML operations
- smart networking solutions
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