CSE DSI Machine Learning Seminar with Ziyue Xu (Nvidia)
Federated Learning: Image, Language, and Beyond
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. In this talk, we will discuss two major aspects of FL: the research towards FL model development, and the tool needed to perform a real-life multi-institute FL study. Specifically, we will cover recent works on personalized FL, vertical FL, and client-contribution, and will illustrate the implementation of FL under various model settings using NVFlare - the NVIDIA Federated Learning Application Runtime Environment.
Dr. Ziyue Xu, IEEE Senior Member, is a Senior Scientist at Nvidia. His research interests lie in the area of image analysis and machine learning with applications in biomedical and clinical imaging, and is among the earliest researchers in adopting deep learning in this field. Before joining Nvidia, he was a Staff Scientist at National Institutes of Health.
Dr. Xu obtained his B.S. from Tsinghua University, and M.S./Ph.D. from the University of Iowa. He is an Associate Editor for the journals of International Journal of Computer Vision (IJCV), IEEE Transactions on Medical Imaging (TMI), IEEE Journal of Biomedical and Health Informatics (JBHI), Computerized Medical Imaging and Graphics (CMIG), and Computers in Biology and Medicine (CBM).