AI Empowered Federated Learning for Industrial Internet of Things

Submission opens on ScholarOne (do not submit before this date): 1st December 2021

Submission closes: 23rd March 2022

Guest editors

Dr Amandeep Kaur, Sri Guru Granth Sahib World University, India

Email: [email protected]

Dr. Ashutosh Sharma, Southern Federal University, Russia

Email: [email protected]

Dr. Poongodi M, Hamad Bin Khalifa University, Doha, Qatar

Email: [email protected]

Overview of special issue

The mobile edge computing and the Internet of Things have been used in recent years in mobile networks that pose an obstacle to the new technical needs. Data transmission and reducing the throughput and network load are the main focus of the technology growth. The network centered on emerging needs for data storage, computation and low latency treatment in potentials, such as smart cities, transport, smart grids and many sustainable environments. Federated Learning (FL) is a distributed Artificial Intelligence-based approach framework that enhances the communication between smart systems with improved network capacity, service quality, networks availability and user experience. Processes in the telecoms, bioinformatics, healthcare, Internet of Things, social networks and manufacturing fields supports advanced mathematical methods in wireless communications with the FL. But research into cutting-edge intelligence is still at its infancy, and the computer system and artificial intelligence communities both want a dedicated location for sharing recent developments in cutting edge intelligence. The aim of this special issue is to disseminate the latest research and innovation in the FL for wireless communication based on artificial intelligence. This special issue provides an insight into the topic of safe communications in the next generation through AI based FL, which shapes wireless communication.

Indicative list of anticipated themes

  • Federated learning and AI approaches for optimizing edge computing networks
  • Distributed and collaborative AI with edge computing and networking
  • AI approaches for unmanned aerial vehicles (UAVs) techniques using MEC
  • Applications of FL based AI approaches for wireless communications technologies using MEC
  • Edge/IoT based wireless communication using FL
  • Deep learning for the management of edge computing networks
  • Transfer learning for the preliminary deployment of Federated Learning models on the edge
  • Training scheme of Federated Learning model at the edge
  • New AI-based edge computing and networking test bed and trials

Submission details

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