IEEE Global Communications Conference
8–10 December 2020 // In-person (Taipei, Taiwan)
7-11 December 2020 // Virtual
Communications for Human and Machine Intelligence

Call for Papers

Machine learning and data-driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. To date, most existing learning solutions for wireless networks have relied on conventional machine learning approaches that require centralizing the training data and inference processes on a single data center or cloud. However, in tomorrow’s wireless 5G systems, due to privacy constraints and limited communication resources for data transmission, it is impractical for all wireless devices that are engaged in learning to transmit all of their collected data to a data center or a cloud that can subsequently use a centralized learning algorithm for data analytics or network self-organization. To this end, distributed edge learning frameworks are needed, to enable the wireless devices to collaboratively build a shared learning model with training their collected data locally.

This workshop aims to bring together academic and industrial researchers in an effort to identify and discuss the major challenges and recent breakthroughs related to edge learning. Topics of interest include but are not limited to the following:

  • Fundamental limits of edge learning systems
  • Wireless network optimization for improving the performance of edge learning
  • Data compression for edge learning
  • Adaptive transmission for edge learning
  • Techniques for wireless crowd labelling
  • Modeling and performance analysis of edge learning networks
  • Energy efficiency of implementing machine learning over wireless edge networks
  • Ultra-low latency edge learning
  • Multi-agent reinforcement learning for intelligent network control and optimization
  • Network architectures and communication protocols for edge learning
  • Experimental testbeds and techniques of edge learning
  • Privacy and security issues of edge learning
  • Edge learning for intelligent signal processing
  • Edge learning for user behavior analysis and inference
  • Edge learning for emerging applications