Applications of AI Empowered Federated Learning Using Mobile Edge computing for IoT

  in Special Issue   Posted on September 28, 2020

Information for the Special Issue

Submission Deadline: Sat 15 May 2021
Journal Impact Factor : 1.061
Journal Name : Wireless Personal Communications
Journal Publisher:
Website for the Special Issue: https://www.springer.com/journal/11277/updates/18399882
Journal & Submission Website: https://www.springer.com/journal/11277

Special Issue Call for Papers:

The recent years have witnessed the usage of mobile edge computing and Internet of Things in mobile networks that provide a bottleneck in the emerging technological needs. The technology development focuses the needs on Data transmission, reducing the throughput and network load. The emerging needs focused the network to handle the data storage, computing and to deal the low latency in potential applications such as smart city, transportation, smart grids and many sustainable environments. The massive amount of data can be handled by the mobile edge computing devices that has the specialised architecture to provide network optimisation, network analytics using IoT. The contributions of Federated Leaning provide a successful architecture to enhance the security in the network with the heterogeneous IoT environments.  AI based Federated Learning (FL) is a bottle neck technology that enhances the privacy and security issues in the wireless paradigm. Federated Learning is a distributed platform of AI based approach that enhances the smart systems connectivity with increased network capacity, quality of service, network availability, and user-experience. Advanced Mathematical tools in the field of wireless communications with the FL helps the process in telecom, bioinformatics, healthcare, Internet of Things, social networks, and manufacturing. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. The main focus of this Special Section is on the most recent applications of Federated Learning using Mobile Edge Computing for IoT to optimize data for next-generation networks. Hence, the goal of this Special Issue is to disseminate the latest research and innovations on Artificial Intelligence based FL for Wireless Communications. This Special issue provides an insight to the readers in way of inculcating the theme that shapes the wireless communication through AI based FL next generation secure communication.Topics of interest include, but are not limited to, the following scope:

  • CR networks secure spectrum allocation Using FL based Mobile Edge computing 
  • Federated  learning applications enabled by edge computing
  • Federated learning and AI approaches for optimizing edge computing networks
  • Federated based traffic offloading prediction and optimization
  • Distributed and collaborative AI with edge computing and networking
  • Hardware platforms and software stacks for deploying Federated Learning on the edge
  • Spectrum Sensing, Spectrum Management and security Using MEC
  • Blockchain and IoT applications Using MEC 
  • Game theory applications for Cognitive and other wireless communication networks using MEC
  • 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
  • Offloading scheme for intensive Federated Learning tasks
  • Architecture and orchestration of Federated Learning services in edge computing
  • 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
  • Segmentation of Federated Learning models for collaborative intelligence between cloud and the edge
  • New AI-based edge computing and networking test bed and trials

Submission deadline: 15th May 2021Deadline for review (typically 4-8 weeks after deadline for submissions): 19th  July 2021Decisions: First Revision: 25th  September 2021Deadline for revised version by authors (typically 4-8 weeks after decisions): 10th  November 2021Deadline for 2nd review (typically 2-4 weeks after deadline for revised versions): 10th December 2021Final decisions: January 2022

Lead Guest EditorS.VimalDepartment of Information Technology, National Engineering College, India  svimalnec@nec.edu.in,svimalphd@gmail.comGuest EditorsShahid MumtazSenior Researcher, Senior Member IEEE, Instituto de Telecomunicaces, Aveiro, Portugalsmumtaz@av.it.ptAli Kashif BashirDepartment of Computing and Mathematics, Manchester Metropolitan University, UKa.ba4shir@mmu.ac.ukDanilo PelusiFaculty of Communication Sciences, University of Teramo, Italydpelusi@unite.itA. SureshDepartment of Computer Science & Engineering, SRM Institute of Science & Technology, Indiaprisu6esh@ieee.orgAuthors should follow the WPC Journal manuscript format described at the journal site. Manuscripts should be submitted online throughhttps://www.editorialmanager.com/wire/default.aspx.

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