Machine Learning for Mobile Networks
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".
Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 5288
Special Issue Editors
Interests: edge computing; machine learning; networking intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent years have disclosed a tremendous increase in interest in smart wireless applications, such as intelligent transportation systems, digital healthcare, and Industry 4.0, among others, from both academia and industry. The end-devices of these applications generate a significant amount of data. Furthermore, there are many scenarios where it is not possible to mathematically model mobile network functions. To address such limitations, one can use machine learning to model mobile network functions for making them smarter. Machine learning will use the data generated by mobile networks for training and thus improve their performance. Machine learning can be divided mainly into two categories, centralized machine learning and federated learning. Centralized machine learning relies on training of the machine learning model at a third-party centralized location, whereas federated learning enables on-device machine learning without the need to migrate the device’s data to the centralized server. Centralized machine learning requires the migration of device data to the centralized server, and this raises privacy concerns. Meanwhile, federated learning faces data heterogeneity issues. The purpose of this SI is to cover both centralized machine learning and federated learning for wireless systems. Suggested topics include but are not limited to the following:
- Federated-learning-based smart applications (e.g., healthcare, intelligent transportation systems);
- Communication efficient federated learning models;
- Incentive mechanism for federated learning;
- Personalized federated learning;
- Deep-learning-based applications;
- Applications of centralized and federated learning in optimization of wireless systems, including radio resources management, caching, edge computing resource management;
- Privacy-aware design for machine learning;
- Robust architecture for federated learning.
Prof. Dr. Choong Seon Hong
Dr. Latif U. Khan
Guest Editors
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Keywords
- machine learning
- federated learning
- centralized machine learning
- mobile network
- wireless systems
- radio resources management
- caching, edge computing resource management
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