Challenges and Opportunities Presented by Federated Learning in Mobile Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 375

Special Issue Editors

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Guest Editor
Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
Interests: security and privacy in mobile edge computing; efficient and private federated learning; data-driven optimization in cyber-physical systems

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Guest Editor
Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
Interests: computer architecture; machine learning; Internet of Things

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Guest Editor
Department of Computer & Information Sciences, Towson University, Towson, MD 21252, USA
Interests: robust and secure deep learning networks; cybersecurity; security and privacy in Internet of Things (IoT); smart transportation; smart city; and smart healthcare.
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Special Issue Information

Dear Colleagues,

With the explosive growth of mobile devices and the rapid development of communication technology, mobile computing has quickly emerged as a new paradigm, bringing computing power to the edge of the network. Due to the increasing need for decentralized and privacy-preserving computation, federated learning (FL) has become a pivotal technique in the realm of mobile computing. As a decentralized machine learning approach, FL allows mobile devices to collaboratively learn a shared model while keeping the training data local, thereby offering potential solutions to challenges inherent to data privacy and bandwidth efficiency. Since mobile devices are utilized in everyday life, a variety of applications have been developed with the integration of FL into mobile computing, including face recognition, personalized recommendations, health monitoring, voice recognition, autonomous driving, etc. Nevertheless, there remain challenges in the deployment of FL in mobile computing, such as issues related to scalability, privacy and security, energy efficiency, and communication efficiency and ensuring model robustness under heterogeneous settings. With this Special Issue, we seek high-quality submissions that highlight recent advances, existing and potential challenges, and opportunities in the field of federated learning in mobile computing.

For this Special Issue, we invite contributions in the form of original research articles as well as comprehensive literature reviews. Research areas of interest include (but are not limited to) the following:

  • Federated learning algorithms for mobile devices;
  • Communication-efficient federated learning;
  • Energy-efficient federated learning;
  • Secure and privacy-preserving mechanisms in federated learning;
  • Challenges in non-IID data distributions in FL;
  • Challenges in heterogeneous data and devices in FL;
  • Scalability and resource management in federated learning;
  • Machine learning and AI for wireless communications;
  • B5G networks and federated learning;
  • Security and privacy issues in mobile computing;
  • Applications of federated learning in mobile computing.

We look forward to receiving your contributions.

Dr. Xinyue Zhang
Dr. Bobin Deng
Dr. Qianlong Wang
Guest Editors

Manuscript Submission Information

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  • federated learning
  • mobile computing
  • security
  • Internet of Things
  • mobile networks

Published Papers

This special issue is now open for submission.
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