IEEE Global Communications Conference
4–8 December 2022 // Rio de Janeiro, Brazil // Hybrid: In-Person and Virtual Conference
Accelerating the Digital Transformation through Smart Communications

WS07: Edge Learning over 5G Mobile Networks and Beyond (EL5GMNB) - VIRTUAL

VIRTUAL PROGRAM

*All times are BRT (GMT-3)

THURSDAY, DECEMBER 8 09:00-10:30

Edge Learning 1

Time: 09:00-10:30 BRT
 
Clustered Federated Learning with Model Integration for Non-IID Data in Wireless Networks
Jingyi Wang and Zhongyuan Zhao (Beijing University of Posts and Telecommunications, China); Wei Hong (Beijing Xiaomi Mobile Software, China); Tony Q. S. Quek (Singapore University of Technology and Design, Singapore); Zhiguo Ding (University of Manchester, United Kingdom (Great Britain))
 
Incentive Mechanism for Federated Learning based on Random Client Sampling
Hongyi Wu (The Chinese University of Hong Kong Shenzhen, China); Xiaoying Tang (The Chinese University of Hong Kong, Shenzhen, China); Ying Jun (Angela) Zhang (The Chinese University of Hong Kong, Hong Kong); Lin Gao (Harbin Institute of Technology, Shenzhen, China)
 
Joint Scheduling and Beamforming Design in Traffic-Aware RIS Aided MEC Network
Aichen Li and Yang Liu (Dalian University of Technology, China); Qingqing Wu (University of Macau, China); Qingjiang Shi (Tongji University, China); Jun Zhao (Nanyang Technological University, Singapore)
 
Federated Learning over LEO Satellite
Yiji Wang and Zou Cheng (Shanghaitech University, China); Dingzhu Wen and Yuanming Shi (ShanghaiTech University, China)
 
Simultaneous Federated Learning and Information Transmission Over Time-Varying MIMO Channels
Xufeng Liu (Beijing University of Posts and Telecommunications, China); Wanli Ni (Beijng University of Posts and Telecommunications, China); Hui Tian (Beijng University of posts and telecommunications, China); Yuan Wu (University of Macau, Macao)

THURSDAY, DECEMBER 8 10:30-12:00

Edge Learning 2

Time: 10:30-12:00 BRT
 
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control
Zixuan Zhang (ShanghaiTech University, China); Yuning Jiang (EPFL, Switzerland); Yuanming Shi and Ye Shi (ShanghaiTech University, China); Wei Chen (Tsinghua University, China)
 
Image Semantic Communications: An Extended Rate-Distortion Theory Based Scheme
Wanjie Tong, Fangfang Liu, Zhengfen Sun, Yang Yang and Caili Guo (Beijing University of Posts and Telecommunications, China)
 
NPSR: Neural Network enabled Phase-Space Reconstruction for Wireless Channel Prediction
Hanhui Li (Shanghaitech University, China); Kai Li (School of Information Science and Technology, ShanghaiTech University, China); Jinhan Guo (Shanghaitech University, China); Yang Yang and Yong Zhou (ShanghaiTech University, China)
 
Joint Offloading and Resource Allocation with Partial Information for Multi-user Edge Computing
Yang Li, Xing Zhang, Yukun Sun and Junlin Liu (Beijing University of Posts and Telecommunications, China); Bo Lei (Beijing Research Institute China Telcom Beijing, China); Wenbo Wang (Beijing University of Posts and Telecommunications, China)
 
Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks
Qi Chang, Baotian Fan and Yanxiang Jiang (Southeast University, China); Fu-Chun Zheng (University of York, United Kingdom (Great Britain) & Southeast University, China); Mehdi Bennis (Centre of Wireless Communications, University of Oulu, Finland); Xiaohu You (National Mobile communication Research Lab., Southeast University, China)

THURSDAY, DECEMBER 8 14:00-15:30

Edge Learning 3

Time: 14:00-15:30 BRT
 
Call Mute Reduction by Reinforcement Learning Based Deployment of ROHC in Next Generation Networks
Swaraj Kumar (Samsung R&D Institute, India); Veerabhadrappa Gadag (Samsung Research Institute Bangalore, India); Vishal Murgai and Siva Kumar M (Samsung R&D Institute, Bengaluru, India)
 
Over-the-Air Gaussian Process Regression Based on Product of Experts
Koya Sato (The University of Electro-Communications, Japan)
 
A Demonstration of Over-the-Air Computation for Federated Edge Learning
Alphan ?ahin (University of South Carolina, USA)
 
A-LAQ: Adaptive Lazily Aggregated Quantized Gradient
Afsaneh Mahmoudi, José Mairton Barros da Silva, Jr. and Hossein Shokri Ghadikolaei (KTH Royal Institute of Technology, Sweden); Carlo Fischione (KTH, Sweden)
 
Blind Asynchronous Over-the-Air Federated Edge Learning
Seyedsaeed Razavikia, Jaume Anguera Peris and José Mairton Barros da Silva, Jr. (KTH Royal Institute of Technology, Sweden); Carlo Fischione (KTH, Sweden)

THURSDAY, DECEMBER 8 15:30-17:00

Edge Learning 4

Time: 15:30-17:00 BRT
 
Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing
Shoma Shimizu (Yokohama National University, Japan); Takayuki Nishio (Tokyo Institute of Technology, Japan); Shota Saito (Yokohama National University & SkillUp AI, Japan); Yoichi Hirose, Chen Yen-Hsiu and Shinichi Shirakawa (Yokohama National University, Japan)
 
Communication-Efficient Federated Bayesian Learning via Client Selection
Jiarong Yang and Yuan Liu (South China University of Technology, China); Rahif Kassab (King's College London, United Kingdom (Great Britain))
 
Evolutionary Deep Q Network for Collaborative Edge Caching
Ming Zhao, Zhenfeng Sun and Mohammad Reza Nakhai (King's College London, United Kingdom (Great Britain))
 
Joint Source-Channel Coding for Efficient Image Transmission: An Information Bottleneck Based Scheme
Lunan Sun, Caili Guo and Yang Yang (Beijing University of Posts and Telecommunications, China)
 
Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning
Zhang Zezhong (The Chinese University of Hong Kong, Hong Kong); Guangxu Zhu (Shenzhen Research Institute of Big Data, China); Shuguang Cui (The Chinese University of Hong Kong, Shenzhen & Shenzhen Research Institute of Big Data, China)

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