博士论文答辩：面向智能边缘设备的高效联邦学习算法设计
日期：2023/08/29 - 2023/08/29
博士论文答辩：面向智能边缘设备的高效联邦学习算法设计
主讲人：Xiaochen Zhou, Ph.D. candidate at UM-SJTU Joint Institute
时间：2023年8月29日（周二）上午10:00
地点：龙宾楼403会议室
讲座摘要
The prosperity of wireless networks brings massive edge devices to the Internet, facilitating various intelligent applications such as smart manufacturing. In these applications, edge devices need to execute machine learning (ML) models locally for some time-sensitive services such as anomaly detection. Training such ML models requires the data generated by edge devices. However, centralized learning in the cloud breaches users’ data privacy while local learning on edge devices cannot obtain models with high generalization performance. To tackle this dilemma, federated learning (FL) is a promising learning paradigm. In this dissertation, four research topics are identified to resolve some key issues of FL over edge devices: 1) memory-efficient FL for kernel k-means; 2) memory-efficient FL for kernel SVM; 3) domain adaptation for compact models on edge devices; 4) FL over noisy data.