2021 - 2ND WORKSHOP ON EDGE LEARNING FOR 5G MOBILE NETWORKS AND BEYOND

Traditional machine learning tends to be centralized in nature (e.g., in the cloud). However, security and privacy concerns as well as the availability of abundant data and computational resources in wireless networks motivate moving learning algorithms deployed on mobile networks towards the network edge. This has led to the emergence of the rapidly growing area of (mobile) edge learning, which integrates two originally decoupled areas: wireless communication and machine learning. It is widely expected that the advancements in edge learning will provide a platform for implementing edge artificial intelligence (AI) in 5G-and-Beyond systems and supporting large-scale problems ranging from autonomous driving to personalized healthcare. Thus, this proposed full-day workshop will seek to bring together researchers and experts from academia, industry, and governmental agencies to discuss and promote the research and development needed to overcome the major challenges that pertain to this cutting-edge research topic.
We seek original completed and unpublished work not currently under review by any other journal/magazine/conference. Topics of interest include, but are not limited to:
Fundamental limits of edge learning systems
Data compression for edge learning
Adaptive transmission for edge learning
Techniques for wireless crowd labelling
Performance analysis of edge learning networks
Energy efficiency of implementing machine learning over wireless edge networks
Ultra-low latency edge learning and inference
Experiments and testbeds on edge learning
Privacy and security issues in edge learning
Edge learning for intelligent signal processing
Edge learning for user behavior analysis and inference
Distributed reinforcement learning for network decision making, network control, and management