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魔域sf:Distributed Edge Learning for Big Data Analytics: Challenges and Trends

来源:通信工程学院          点击:
报告人 郭嵩 时间 7月20日9:00
地点 腾讯会议直播 报告时间

魔域私服外挂 www.fotosup.com 讲座名称Distributed Edge Learning for Big Data Analytics: Challenges and Trends

讲座时间:2020-07-20 9:00

讲座人:郭嵩

讲座地点:腾讯会议直播(会议ID:241 693 631 密码:200720 链接:https://meeting.tencent.com/s/pDoW8QDU3Rsl


讲座人介绍:

Song Guo is a Full Professor and Associate Head at Department of Computing, The Hong Kong Polytechnic University. His research interests are mainly in the areas of big data, cloud computing, mobile computing, and distributed systems. He is the recipient of the 2019 IEEE TCBD Best Conference Paper Award, 2018 IEEE TCGCC Best Magazine Paper Award, 2017 IEEE Systems Journal Annual Best Paper Award, and other 6 Best Paper Awards from IEEE/ACM conferences. His work was also recognized by the 2016 Annual Best of Computing: Notable Books and Articles in Computing in ACM Computing Reviews. Prof. Guo was an IEEE ComSoc Distinguished Lecturer (2017-2018) and served in IEEE ComSoc Board of Governors (2018-2019). He has also served as General and Program Chair for numerous IEEE conferences.  Prof. Guo is an IEEE Fellow, the Editor-in-Chief of IEEE Open Journal of the Computer Society, and Associate Editor of IEEE Transactions on Cloud Computing, IEEE Transactions on Sustainable Computing, and IEEE Transactions on Green Communications and Networking.


讲座内容:

When accessing cloud-hosted modern applications, users often suffer a significant latency due to the long geo-distance to the central cloud. Edge computing thus emerges as an alternative paradigm that can reduce this latency by deploying services close to users. In this talk, we will analyze the methodology and limitations of popular approaches for supporting AI services on geo-distributed systems along the evolution from cloud computing to edge computing. In particular, we shall discuss how to deal with different sets of challenges in distributed machine learning over heterogeneous geo-distributed systems. We shall also present our recent studies on parameter-server based framework among networked collaborative edges.


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