IEEE GLOBECOM Workshop on Edge Learning over 5G Mobile Networks and Beyond
4-8 December 2023 // Kuala Lumpur, Malaysia
Call for Workshop Papers
Nowadays, standard machine learning approaches require centralizing the training data on a single data center or cloud. Since massive data samples need to be uploaded to the data center, transmission delay can be very high and user privacy is not guaranteed in standard centralized machine learning approaches. However, low-latency and privacy requirements are important in the emerging application scenarios, such as unmanned aerial vehicles, extended reality (XR) services, autonomous driving, which makes centralized machine learning approaches inapplicable. Moreover, due to limited communication resources, it is impractical for all the wireless devices that are engaged in learning to transmit all of their collected data to a data center that uses a centralized learning algorithm for data analytic or network self-organization. Therefore, it becomes increasingly attractive to deploy learning algorithms at edge devices, called edge learning. Suitable topics for this workshop include, but are not limited to, the following areas:
- Secrecy of edge learning algorithms
- Fundamental limits of edge learning systems
- Data compression for edge learning
- Techniques for wireless crowd labelling
- Performance analysis of edge learning networks
- Energy efficiency of implementing machine learning over wireless edge networks
- Over-the-air computation for edge learning
- 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
- Distributed reinforcement learning for network decision making, network control, and management
Steering Committee
- Lajos Hanzo, School of Electronics and Computer Science, University of Southampton, U.K. (lh@ecs.soton.ac.uk).
- Merouane Debbah, Technology Innovation Institute in Abu Dhabi, United Arab Emirates (merouane.debbah@tii.ae).
Workshop Co-chairs
- Mingzhe Chen, Electrical and Computer Engineering Department, University of Miami, US. (mingzhe.chen@miami.edu)
- Changsheng You, Department of Electronic and Electrical Engineering, Southern University of Science and Technology, China (youcs@sustech.edu.cn).
- Christopher Greg Brinton, Elmore Family School of Electrical and Computer Engineering, Purdue University, USA, (cgb@purdue.edu).
TPC Co-chairs
- Viet Quoc Pham, School of Computer Science and Statistics, Trinity College Dublin, Ireland (viet.pham@tcd.ie).
- Zhaohui Yang, Zhejiang Lab, College of Information Science and Electronic Engineering, Zhejiang University, China. (yang_zhaohui@zju.edu.cn)
- Guangxu Zhu, Shenzhen research institute of big data, China (gxzhu@sribd.cn).
Keynote Speakers
- Dusit (Tao) Niyato, Nanyang Technological University, Singapore
- Angela Yingjun Zhang, The Chinese University of Hong Kong, China
Submission Guidelines
- Paper submission: July 5, 2023
- Acceptance notification: September 15, 2023
Submission Guidelines
The workshop accepts only original and previously unpublished papers. All submissions must be formatted in standard IEEE camera-ready format (double-column, 10pt font). The maximum number of printed pages is six including figures without incurring additional page charges (6 pages plus 1 additional page allowed with a charge for the one additional page of USD 100 if accepted)
