IEEE International Conference on Communications
16–20 May 2022 // Seoul, South Korea // Hybrid: In-Person and Virtual Conference
Intelligent Connectivity for Smart World

WS-13: On-Demand Program

WS-13: Edge Learning for 5G Mobile Networks and Beyond

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Presentation Sessions (on-demand):

Session 1

Monday May 16, 2022, 09:00~17:00 (KST), WS 13-01: Edge Learning for Wireless Communications

  1. Joint AMC and Resource Allocation for Mobile Wireless Networks Based on Distributed MARL

Yingzhi Huang (College of Information Science and Electronic Engineering & Zhejiang University, China); Zhaoyang Zhang (Zhejiang University, China); Jue Wang (Zhejiang University, China); Chongwen Huang (Zhejiang University, China); Caijun Zhong (Zhejiang University, China)

  1. Coded Caching via Federated Deep Reinforcement Learning in Fog Radio Access Networks

Yingqi Chen (Southeast University, China); Yanxiang Jiang (Southeast University, China); Fu-Chun Zheng (Harbin Institute of Technology, Shenzhen, China & University of York, United Kingdom (Great Britain)); Mehdi Bennis (Centre of Wireless Communications, University of Oulu, Finland); Xiaohu You (National Mobile communication Research Lab., Southeast University, China)

  1. A clustered learning framework for host based intrusion detection in container environment

Jingfei Shen (University of Science and Technology of China, China); Fanping Zeng (University of Science and Technology of China, China); Weikang Zhang (University of Science and Technology of China, China); Yufan Tao (University of Science and Technology of China, China); Shengkun Tao (University of Science and Technology of China, China)

  1. Few-Shot Learning in Wireless Networks: A Meta-Learning Model-Enabled Scheme

Kexin Xiong (Beijing University of Posts and Telecommunications, China); Zhongyuan Zhao (Beijing University of Posts and Telecommunications, China); Wei Hong (Beijing Xiaomi Mobile Software, China); Mugen Peng (Beijing University of Posts and Telecommunications, China); Tony Q. S. Quek (Singapore University of Technology and Design, Singapore)

  1. Edge-distributed Coordinated Hyper-Parameter Search for Energy Saving SON Use-Case

Hasan Farooq (Ericsson, USA); Julien Forgeat (Ericsson, USA); Shruti Bothe (Ericsson, USA); Maxime Bouton (Ericsson, USA); Per Karlsson (Ericsson, Sweden)

  1. Bird's-eye View Social Distancing Analysis System

Zhengye Yang (Rensselaer Polytechnic Institute, USA); Mingfei Sun (Columbia University, USA); Hongzhe Ye (Columbia University, USA); Zihao Xiong (Columbia University, USA); Gil Zussman (Columbia University, USA); Zoran Kostic (Columbia University, USA);

 

Session 2

Monday May 16, 2022, 09:00~17:00 (KST), WS 13-02: Federated Learning

  1. Over-the-Air Computation for Vertical Federated Learning

Xiangyu Zeng (ShanghaiTech University, China); Shuhao Xia (ShanghaiTech University, China); Kai Yang (JD Technology Group, China); Youlong Wu (ShanghaiTech University, China); Yuanming Shi (ShanghaiTech University, China)

  1. Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design

Shaoming Huang (ShanghaiTech University, China); Pengfei Zhang (ShanghaiTech University, China); Yijie Mao (ShanghaiTech University, China); Lixiang Lian (ShanghaiTech University, China); Youlong Wu (ShanghaiTech University, China); Yuanming Shi (ShanghaiTech University, China)

  1. Client Selection for Asynchronous Federated Learning with Fairness Consideration

Hongbin Zhu (ShanghaiTech University, China); Miao Yang (Shanghai Advanced Research Institute (SARI), Chinese Academy of Sciences (CAS), China); Junqian Kuang (ShanghaiTech University, China); Hua Qian (Shanghai Advanced Research Institute, Chinese Academy of Sciences, China); Yong Zhou (ShanghaiTech University, China)

  1. Best Effort Voting Power Control for Byzantine-resilient Federated Learning Over the Air

Xin Fan (Beijing Jiaotong University, China); Yue Wang (George Mason University, USA); Yan Huo (Beijing Jiaotong University, China); Zhi Tian (George Mason University, USA)

  1. Federated Learning Enabled Channel Estimation for RIS-Aided Multi-User Wireless Systems

Wenhan Shen (Queen Mary University of London, United Kingdom (Great Britain)); Zhijin Qin (Queen Mary University of London, United Kingdom (Great Britain)); A Nallanathan (Queen Mary University of London, United Kingdom (Great Britain))

  1. Federated Learning Cost Disparity for IoT Devices

Sheeraz A. Alvi (The Australian National University, Australia); Yi Hong (Monash University, Australia); Salman Durrani (The Australian National University, Australia)

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