报告题目：Enable Intelligence on Billion Devices with Deep Learning 报告人：马里兰大学 李昂教授 时间：2022年6月22日（周三），上午10:00-11:00 地点：腾讯会议 784-649-184 联系人：徐跃东 老师 欢迎感兴趣的老师和同学参加！ 摘要：The proliferation of edge devices and the gigantic amount of data they generate are distributed everywhere. Such distributed data fuel the intelligence at the edge where data reside. In this talk, I will present my works on how to enable intelligence on large-scale edge devices by leveraging the power of deep learning. First, I will present my work on designing a task-agnostic privacy-respecting data crowdsourcing framework for learning a feature extractor that can hide user-specified private information from intermediate representations while retaining the high utility of extracted features. Those features can be safely aggregated to train deep neural works for any learning tasks. Second, I will shift from the centralized setting to the distributed setting for the collaborative learning on edge devices. In particular, I will present my work on designing a personalized federated learning system that can jointly improve communication and computation efficiency. I will also outline future research directions for building billion-scale networked and trustworthy intelligent ecosystem, such as developing ambient intelligent applications, designing scalable and adaptive machine learning algorithms, intelligently employing heterogeneous hardware resources, etc. 简历：Ang Li will join the Department of Electrical and Computer Engineering at University of Maryland College Park in Fall 2023, and before that he will join Qualcomm AI as a Research Associate for one year. He obtained his Ph.D. from Duke University under the supervision of Prof. Yiran Chen. His research interests lie in the intersection of machine learning and edge computing, with a focus on building large-scale networked and trustworthy intelligent systems to solve practical problems in a collaborative, scalable, secure, and ubiquitous manner. He received the Best Student Paper Award in KDD’20. His research is also applied to commercial products by companies.
