主题：Federated Learning Systems: Towards Effective and Efficient Machine Learning Systems on Data Silos

嘉宾：何丙胜 新加坡国立大学  教授  计算机学院副院长

          李钦宾  加州大学伯克利分校  博士后研究员

时间：2023年10月11日 上午10:00 – 11:30

地点：18luck新利电竞 东五楼210学术报告厅

报告摘要：

Federated learning has been a hot research area in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Just like deep learning systems such as Caffe, PyTorch, and Tensorflow that boost the development of deep learning algorithms, federated learning systems are equivalently important, and face challenges from various issues such as unpractical system assumptions, scalability and efficiency. Inspired by federated systems in other fields such as databases and cloud computing, we study the system design requirements for federated learning systems. We find that two important features for federated systems in other fields, i.e., heterogeneity and autonomy, are rarely considered in the existing federated learning systems. In this talk, we will take a systematic comparison among the existing federated learning systems and present our research progress and future research opportunities and directions for data systems. More details about our research can be found at http://www.comp.nus.edu.sg/~hebs/ and related survey (https://arxiv.org/abs/1907.09693).

报告人简介:

Dr. Bingsheng He is currently a Professor and Vice-Dean (Research) at School of Computing, National University of Singapore. Before that, he was a faculty member in Nanyang Technological University, Singapore (2010-2016), and held a research position in the System Research group of Microsoft Research Asia (2008-2010), where his major research was building high performance cloud computing systems for Microsoft. He got the Bachelor degree in Shanghai Jiao Tong University (1999-2003), and the Ph.D. degree in Hong Kong University of Science & Technology (2003-2008). His current research interests include cloud computing, database systems and high performance computing. He has been a winner for industry faculty awards from Microsoft/NVIDIA/Xilinx/Alibaba. His work also won multiple recognitions as “Best papers” collection or awards in top forums such as SIGMOD 2008, VLDB 2013 (demo), IEEE/ACM ICCAD 2017, PACT 2018, IEEE TPDS 2019, FPGA 2021 and VLDB 2023 (industry). Since 2010, he has (co-)chaired a number of international conferences and workshops, including IEEE CloudCom 2014/2015, BigData Congress 2018, ICDCS 2020 and ICDE 2024. He has served in editor board of international journals, including IEEE Transactions on Cloud Computing (IEEE TCC), IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS), IEEE Transactions on Knowledge and Data Engineering (TKDE), Springer Journal of Distributed and Parallel Databases (DAPD) and ACM Computing Surveys (CSUR). He is an ACM Distinguished member (class of 2020).

Dr. Qinbin Li is currently a Postdoctoral Researcher at UC Berkeley under the esteemed guidance of Prof. Dawn Song. Before ascending to his postdoctoral role, he obtained his Ph.D. degree in Computer Science from National University of Singapore in 2022, where he was advised by Prof. Bingsheng He. Tracing back to his foundational education, Dr. Li graduated with a Bachelor's Degree from ACM Class, Huazhong University of Science and Technology in 2018, receiving mentorship from Prof. Hai Jin and Prof. Song Wu. Dr. Li was honored with the Google PhD Fellowship in 2021. His current research interests lie in machine learning, federated learning, privacy, high-performance computing, and systems.