Special Issue on Robust Federated Learning over Future Wireless Networks

Machine learning and information-based networking methods are now highly regarded as vital facilitators for the next generation's intelligent networks. Most existing wireless network learning systems are based on the centralisation of training and inference processes by transferring data to data centres from advanced smartphones. 

Such a centralised approach can lead to privacy issues, breach mobile applications' latency restrictions, or become ineffective due to bandwidth or border capacity constraints. Federated machine learning at the network edge is an exciting approach to solving these issues; it provides edge devices that collaboratively train a locally-standard model using mobile data generated in real-time. But distributed preparation and deduction involve connectivity through wireless connections between wireless devices and edge servers. 

This special issue aims to collect cutting-edge papers on federated learning from academia and industry. The goal is to advance the development of fundamental theories for communication-efficient learning over wireless and the application of federated learning algorithms to optimise wireless networks. 

Topics Covered:

• New theories and techniques such as zero trust access, cloud/edge computing, age of information, and multipath diversity for improving the performance of federated learning.
• New views on communication- and learning-integrated algorithms for realising an intelligent edge.
• Adaptive optimisation of wireless networks for improving the performance of federated learning.
• Revolutionary network protocol design for federated learning.
• Federated learning for intelligent data processing, signal processing, signal detection and estimation.
• Joint communication, computing and sensing for federated learning.
• New network architectures for supporting federated learning.
• Privacy and security issues of federated zero trust access.
• Federated learning in emerging applications, such as the Internet of Things, autonomous vehicle systems, intelligent reflecting surfaces and virtual reality systems.
• 5G/6G testbeds for federated learning

Important Deadlines:

• Submission deadline: 30 August 2021

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online; please select SI: Robust Federated Learning over Future Wireless Networks on submission.

Guest Editors:

• Dr. Sandeep Verma, Dr. BR Ambedkar National Institute of Technology, India. Email: sandeepverma@ieee.org  
• Dr. Bahaa Al-Musawi, University of Kufa, Iraq. Email: bahaa.almusawi@uokufa.edu.iq  
• Managing Guest Editor: Dr. Shiva Raj Pokhrel, Deakin University, Australia. Email: shiva.pokhrel@deakin.edu.au