Secure and Energy-Efficient Federated Learning over Wireless Networks: Cooperative Vehicle Infrastructure System

Guest Editors:
Arvind Dhaka: Associate Professor, Manipal University Jaipur, India
Gang Wu: University of Electronic Science and Technology of China, China
Weidang Lu: Zhejiang University of Technology, China
Imran Shafique Ansari: University of Glasgow, UK

Submission Status: Open | Submission Deadline: 30 September 2023

EURASIP Journal on Wireless Communications and Networking is calling for submissions to our Collection Secure and Energy-Efficient Federated Learning over Wireless Networks: Cooperative Vehicle Infrastructure System.

The purpose of this call for papers is to solicit original research contributions on secure and energy-efficient federated learning over wireless networks. Federated learning, a rapidly growing field of study, is an approach to machine learning (ML) that allows data to remain on its original source while still being accessible for centralized training. Federated learning is becoming increasingly popular due to its ability to provide ML solutions without the need to collect, store and share data across multiple sites. Federated learning over wireless networks employs advanced encryption techniques to ensure secure transfer of data between the participants. The use of wireless communication networks ensures that the energy consumption of the distributed devices is minimized. Federated learning over wireless networks can easily scale up as the number of devices connected to the network increases. This ensures that the system is able to handle large amounts of data efficiently without any degradation in performance. Wireless networks are a popular choice for federated learning deployments, as they enable the sharing of data between multiple nodes and allow for dynamic reconfiguration. However, federated learning over wireless networks faces several security and energy efficiency challenges. On the security front, wireless networks are prone to attack, and network nodes must be secured to protect the data and ML models. On the energy efficiency front, the wireless network must be designed to reduce energy consumption while still providing a reliable and secure service.

Submit to Collection

About the collection

We invite authors to submit original research contributions addressing security and energy efficiency for federated learning over wireless networks. Potential topics include, but are not limited to:

• Secure distributed ML algorithms for federated learning over wireless networks
• Secure and energy-efficient protocols for federated learning over wireless networks
• Communication and computation overheads of federated learning over wireless networks
• Optimization of federated learning over wireless networks
• Optimization algorithms for distributed machine learning over wireless networks
• Machine learning-enabled channel coding and resource allocation
• Cognitive radio networks for federated learning
• Mobile edge computing for federated learning
• Secure and efficient cross-domain federated learning schemes
• Secure and energy-efficient federated learning in 5G and beyond
• Federated learning for vehicle-to-vehicle communications.
• Federated learning for connected vehicle infrastructure systems.

Image credit: © Blue Planet Studio / stock.adobe.com

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection, please select "Secure and Energy-Efficient Federated Learning over Wireless Networks: Cooperative Vehicle Infrastructure System" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.