2023-10-18
浏览次数：228报告题目：Federated Learning: Communication & Computation Efficiency
报告人: 周志安（Chee-Onn Chow）博士，马来亚大学电子工程系系主任，副教授
报告时间：2023年10月20日周五11：00-12:00
报告地点：腾讯会议ID：350-403-867
主持人： 柯峰（电子与信息学院）
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摘要：
Federated Learning is a much-needed technology in this golden era of big data and Artificial Intelligence, due to its vital role in preserving data privacy, and eliminating the need to transfer and process huge amounts of data, while maintaining the numerous benefits of Machine Learning. As opposed to the typical central training process, Federated Learning involves the collaborative training of statistical models by exchanging learned parameter updates. However, wide adoption of the technology is hindered by the communication and computation overhead forming due to the demanding computational cost of training, and the large-sized parameter updates exchanged. In popular applications such as those involving Internet of Things, the effects of the overhead are exacerbated due to the low computational prowess of edge and fog devices, limited bandwidth, and data capacity of internet connections. In this lecture, we provide a brief overview on the concept of federated learning, and possible ways to improve its communication and computation efficiency.
个人简历
Chee-Onn Chow（周志安） is the Head of the Department of Electrical Engineering, University of Malaya. He is the Senior Member of IEEE. He received his Doctorate of Engineering from the Tokai University, Japan in 2008. He has published more than 50 journal and conference papers and completed more than 20 research projects funded by national and international organizations. His research interests include various issues related to wireless communications, multimedia applications, machine learning and data analytics.