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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: Closed   |   Submission Deadline: Closed

This collection is no longer accepting submissions


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.

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

  1. The rapid development of infinite networks and information technology has promoted the wide deployment and rapid growth of intelligent interactive devices. However, at the same time, touch interaction technolo...

    Authors: Chang Zhao and Linghao Zhang
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:107
  2. The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection...

    Authors: Yu Han and Xiaowei Zhu
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:101
  3. In this work, we present a blockchain-based federated learning (FL) framework that aims achieving high system efficiency while simultaneously addressing issues relating to data sparsity and the disclosure of p...

    Authors: Jiayong Chai, Jian Li, Muhua Wei and Chuangying Zhu
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:100
  4. Efficient utilization of network resources, particularly channel bandwidth allocation, is critical for optimizing the overall system performance and ensuring fair resource allocation among multiple distributed...

    Authors: Miaoxin Xu
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:97
  5. In this paper, the potential for conserving energy has been used inside the collaborative network. The purpose of this work is to examine a model of a collaborative mobile cloud with the objective of lowering ...

    Authors: ErQiang Dong and Hengchuan Guo
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:87
  6. The essence of the Internet of Vehicles is a social and physical information system, including the psychological and organizational factors of human beings. The complexity of the Internet will lead to certain ...

    Authors: Chengzhang Liang
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:75
  7. In-vehicle network intrusion detection tasks, it is usually necessary to simultaneously meet the requirements of low computational power consumption, real-time response, and high detection accuracy. In respons...

    Authors: Xueli Wang and Qin Wang
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:70
  8. Nodes in performance heterogeneous wireless sensor networks (HWSNs) often have varying levels of available energy, storage space, and processing power due to the network’s limited resources. Additionally, cove...

    Authors: Pingzhang Gou, Baoyong Guo and Miao Guo
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:59

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.