为进一步提升研究生科研水平和创新能力，提升国际化视野，鼓励研究生在通信领域开展创新研究，促进学科交叉融合发展，提升科研育人效果，吉林大学通信工程学院邀请了英国拉夫堡大学（Loughborough University）刘小兰博士为学院师生做学术报告，学术报告采取线上线下混合方式，具体信息如下：
报告主题：分布式学习在无线通信领域中的应用
报告时间：2021年12月31日上午9点
线上腾讯会议链接：https://meeting.tencent.com/dm/V09wOaDyHIHj
会议号：331-300-681
密码：211231
线下会场：南湖校区一教125
欢迎广大师生踊跃参加！
报告人主讲内容与简介：
Dr. Xiaolan Liu, Assistant professor, Loughborough University
Topic: Distributed learning techniques in wireless communications
Abstract: Distributed learning techniques can efficiently support machine learning model training by exploiting the distributed computational resources. In this talk, distributed learning in wireless networks will be discussed, with focus on the most recent distributed learning approaches. Considering the diversity of wireless users with different resources, implementing distributed learning techniques in wireless networks is challenging. This talk starts from a comprehensive overview of the state-of-art distributed learning architectures in wireless communications. Then recent work on designing new hybrid distributed learning architectures and user scheduling schemes to address the challenges of diverse users in wireless networks is introduced. Moreover, energy-efficient user scheduling to improve energy and computation efficiency of wireless users will also be discussed.
Short Bio: Dr. Xiaolan Liu is a lecturer (Assistant professor) at the Institute of Digital Technologies at Loughborough University in the London campus. She is also a visiting scholar at King’s College London (KCL) and The Hong Kong University of Science and Technology (HKUST). She received her PhD degree in Computer Science from Queen Mary University of London (QMUL) in July 2021. She was a research associate in KCL from August 2020 to July 2021. She received her B. Eng and M. Eng degrees in Communication Engineering from Jilin University in 2014 and 2017. Her current research interests include distributed learning for wireless communications, reinforcement learning in edge computing, Internet of Things, and privacy-preserving machine learning.
