WELCOME TO THE 2nd WORKSHOP ON "EDGE MACHINE LEARNING FOR 5G MOBILE NETWORKS AND BEYOND"
14 June 2021 (Monday), Montreal, Canada
STEERING COMMITTEE MEMBERS:
- Prof. Shuguang Cui, IEEE Fellow, the Chinese University of Hong Kong, Shenzhen, China (email@example.com).
- Prof. Ayfer Özgür, IEEE Senior Member, Stanford University, Stanford, CA, USA (firstname.lastname@example.org).
- Prof. H. Vincent Poor, IEEE Fellow, Princeton University, NJ, USA (email@example.com).
- Mingzhe Chen, Princeton University, NJ, USA and the Chinese University of Hong Kong, Shenzhen, China (firstname.lastname@example.org).
- Shiqiang Wang, IBM T. J. Watson Research Center, USA (email@example.com).
- Zhaohui Yang, King's College London, UK (firstname.lastname@example.org).
- Prof. Wei Yu, University of Toronto, Canada.
- Prof. Zhi Ding, University of California Davis, USA.
- Prof. Walid Saad, Virginia Tech, USA.
SCOPE AND TOPICS OF THE WORKSHOP
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We seek original completed and unpublished work not currently under review by any other journal/magazine/conference.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
|Paper submission||January 20, 2021|
|Notification of acceptance||February 20, 2021|
|Camera-ready papers||March 1, 2021|
|Workshop date||June 14, 2021|