应我校电子与信息工程学院孟维晓教授、韩帅教授、陈舒怡助理教授邀请，在哈尔滨工业大学国际合作部、电信学院青年教师联合会、IEEE ComSoc 哈尔滨分会、IEEE VTS哈尔滨分会、IEEE BTS哈尔滨分会、中国电子学会信号处理分会、IEEE IoT-AHSN技术委员会的支持下，香港科技大学（广州） Prof. Dirk KUTSCHER、Prof. Gareth TYSON、Prof. Ying CUI、Prof. Zijun GONG、Prof. Hong XING将于2023年8月31日9:00-17:00，在2A 1011进行线下学术讲座。欢迎对此感兴趣的专家学者、老师和同学参加。
报告题目1：Towards Communication-Efficient Over-the-Air Federated Learning: Wireless Implementations and Convergence Analysis
Abstract: With the proliferation of geo-distributed IoT devices and edge/cloud-computing applications over siloed data centres, renewed interest is gained in distributed learning, especially federated learning (FL), where multiple devices collaboratively train a shared model w/o explicit exchange of local data, thus reducing communication load while preserving users’ privacy. However, when wireless communications attempt to accommodate such model related information exchange in the last mile, communication efficiency, in the presence of trendy models with over millions of parameters, easily becomes “Achilles' heel”. To combat such issues, over-the-Air (AirComp) FL is well known to enable multiple devices to simultaneously access to the edge server over the same bandwidth resources for model aggregation. While AirComp FL does make collaborative training scalable, it encounters many challenges in implementations. In this talk, we aim for addressing some of them, namely, the mismatch between dimensions of model parameters and available wireless resources, and limitations of deploying AirComp FL in parameter-server (PS) topology. First, we introduce AirFL-Clip-Comp, which advocates dimension reduction leveraging compressive sensing and simplified power control for uncoded transmission. Next, we investigate AirFL-D2D, where novel routing of wireless D2D transmissions is proposed to enable analog implementation of decentralized stochastic gradient decent (DSGD). Both implementations are provided with convergence analysis, with the former highlighting its evaluation being free from realization-specific parameters, and the latter revealing the impact of connectivity and signal-to-noise ratio (SNR), respectively. Finally, theoretical insights are verified by experiments with future directions being identified.
演讲者：Dr. Hong XING received the B.Eng. degree in Electronic Sciences and Technologies from Zhejiang University, China, and the Ph.D. degree in Wireless Communications from King‘s College London, U.K.. Since Jan. 2022, she has been an Assistant Professor with the IoT Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), China, and an Affiliate Assistant Professor with the Dept. of ECE, The Hong Kong University of Science and Technology, HK SAR. From Mar. 2019 to Dec. 2021, she was appointed as Research Associate Professor with Shenzhen University, China. Prior to that, she was a Post-Doctoral Research Fellow with Queen Mary University of London, U.K., and King’s College London, U. K., respectively, between Feb. 2016 and Jan. 2019. Her research interests include edge learning/inference, mobile-edge computing, simultaneous localization and communication, and wireless information and power transfer. Her research output has been frequently published in IEEE top-venue journals and conferences including two ESI highly cited papers. She received the Best 50 of IEEE Global Communications Conference (GLOBECOM’14) in 2014. She was an Associate Editor of IEEE ACCESS between Mar. 2019 and Mar. 2023. She has regularly served as Member of TPC for prestigious IEEE Conferences such as GLOBECOM, ICC and VTC etc.
报告题目2：OTFS调制技术简介
Abstract: 在过去的30年里，OFDM调制技术取得了巨大的成功。通过把宽带的频选信道分割成平坦子信道（即相干带宽），OFDM能够在每个子信道上去除符号间干扰。它也因此被称为“最优雅的去符号间串扰方法”。OFDM的优点和缺点，都来自于同一个源头，那就是把无线信道建模成一个线性时不变（LTI，Linear Time Invariant）的系统。可实际的信道必然是时变的。OFDM的解决方案是在时间上把信道切割成小段，在每个小段内都可以认为信道基本不变，即准静态信道（小于相干时间）。当我们把这一折衷的方案应用在快速变化的信道里，就不可避免地要付出巨大的代价。在美国得克萨斯大学奥斯汀分校（University of Texas at Austin）的Ronny Hadani教授看来，这一代价是BER的增大。因为估计的信道信息随着时间的推移不再准确。他因此在2017年提出了一种新的调制方式OTFS（Orthogonal Time Frequency Space）来解决这一问题。在今天的讨论中，我们将从另一个视角来分析基于LTI信道的OFDM调制的缺陷，以及OTFS的必要性。具体来说，OFDM在高动态信道下需要非常频繁地进行信道估计，导致了频谱效率的急剧下降。这正是OTFS的必要性所在。
演讲者：巩紫君 (Member, IEEE) 分别于2013年和2015年在哈尔滨工业大学获得了通信工程的本科和信息与通信工程的硕士学位。2021年，在纽芬兰纪念大学（Memorial University of Newfoundland, MUN）获得了计算机工程的博士学位。2021年5月至12月，在加拿大滑铁卢大学（University of Waterloo）的沈学民教授（Sherman Xuemin Shen）课题组担任博士后研究员。2022年1月加入了香港科技大学（广州），担任物联网学域的助理教授至今。主要研究方向包括统计信号处理和优化，主要应用有Massive MIMO、毫米波通信、信道估计、毫米波雷达、声纳信号处理、通信感知一体化、通信定位一体化、定位和导航技术。 他获得了2017年在新加坡举办的IEEE GLOBECOM’17的Best Paper Award。
报告题目3：Systems Challenges in the Decentralized Web
Abstract: The "Fediverse" has recently seen a renewed momentum, with a number of Fediverse platforms like Mastodon gaining increasing traction (partly due to the controversial acquisition of Twitter by Elon Musk). These platforms offer alternatives to traditional 'centralised' web platforms like Twitter and YouTube by enabling the operation of web infrastructure and services without centralised ownership or control. They do, however, raise several key systems challenges related to performance, security and resilience. In this seminar, I will present some recent work evaluating and building decentralized web services, focusing on the challenges encountered in-the-wild. The presentation is based on several papers recently published at SIGCOMM, SIGMETRCS, IMC and WWW.
Bio: Prof. Gareth TYSON is an Assistant Professor at Hong Kong University of Science and Technology (GZ). His research is primarily in the field of Internet measurements, with a focus on networking, web and social computing. He uses empirical methods to evaluate the properties of such systems, and then builds on this to propose design innovations. He regularly publishes in venues such as SIGCOMM, SIGMETRICS, WWW, INFOCOM and IMC. He has been awarded the IRTF Applied Networking Research Award 2023; Distinguished TPC Member at CoNEXT 2022; WOAH Shared Task on Hateful Memes Prize 2021; the Best Student Paper Award at the Web Conference 2020; the Best Paper Award at eCrime'19; the Honourable Mention Award at the Web Conference 2018 (best paper in track); and the Best Presentation Award at INFOCOM'18. He received QMUL's Faculty Research Excellence Award in 2021 and the Brendan Murphy Memorial Young Researcher Prize in 2013. He has served on the organising and program committees for numerous top-tier conferences, including ACM SIGCOMM, SIGMETRICS, WWW and IMC.
报告题目4：Optimal Design for Wireless Communications, Networking, Signal Processing, and Multimedia Transmissions
Abstract: This talk presents recent research in optimization and learning and their applications. The topics include massive machine-type communications (mMTC), intelligent reflecting surface (IRS), mobile edge computing and caching, cross-layer control, distributed/federated learning, immersive video streaming, etc. The results demonstrate the critical roles of optimization and learning in various fields.
Bio: Prof. Ying Cui received her B.Eng degree in Electronic and Information Engineering from Xi’an Jiao Tong University, China, in 2007, and her Ph.D. degree from the Hong Kong University of Science and Technology, Hong Kong, in 2012. She held visiting positions at Yale University, US, in 2011 and Macquarie University, Australia, in 2012. From June 2012 to June 2013, she was a postdoctoral research associate at Northeastern University, US. From July 2013 to December 2014, she was a postdoctoral research associate at the Massachusetts Institute of Technology, US. From January 2015 to July 2022, she was an Associate Professor at Shanghai Jiao Tong University, China. Since August 2022, she has been an Associate Professor with the IoT Thrust at The Hong Kong University of Science and Technology (Guangzhou) and an Affiliate Associate Professor with the Department of ECE at The Hong Kong University of Science and Technology. Her current research interests include optimization, learning, IoT communications, mobile edge caching and computing, multimedia transmission, etc. She was selected to the Thousand Talents Plan for Young Professionals of China in 2013. She received Best Paper Awards from IEEE ICC 2015 and IEEE GLOBECOM 2021. She serves as an Editor for the IEEE Transactions on Wireless Communications.
报告题目5: Computing in the Network: Challenges and Research Opportunities
Abstract: Distributed Machine Learning, social AR/VR and other emerging applications raise the question of how networks can support distributed applications better. This talk discusses the potential and the challenges for "Computing in the Network" – an attempt to integrate computing and networking to create more elastic and resilient infrastructure for applications. We review the state of the art for Computing in the Network and introduce a new research direction called "Compute-First Networking".
Bio: Prof. Dirk Kutscher is a professor at the Hong Kong University of Science and Technology in Guangzhou, China – HKUST(GZ), where is co-directing the Future Networked Systems Laboratory (FNSL). Dirk's main interests lie in the intersection of distributed computing and networking and in Internet architecture. Recently, Dirk has initiated a new research direction called "Compute-First Networking" towards re-imaging the relationship of networking and computing. Previously he has been a professor for computer science and network at the University of Applied Sciences in Emden/Leer, Germany, the CTO for Virtual Networking and IP at Huawei's German Research Center, the Chief Researcher for Networking at NEC Laboratories Europe, and a Visiting Researcher at KDDI R&D Laboratories in Japan. He is co-chairing two Research Groups in the Internet Research Task Force (IRTF) on Information-Centric Networking (ICNRG) and on Decentralized Internet Infrastructure (DINRG). Dirk has published several IETF RFCs, books, and research publications on Internet technologies. He holds a PhD degree from Universität Bremen, Germany and has been working on making the Internet work better since his PhD study times.
