Federated learning (FL) is a trending framework to enable multi-institutional collaboration in machine learning without sharing raw data. This presentation will discuss our ongoing progress in designing FL algorithms that embrace the data heterogeneity properties for distributed data analysis in the FL setting. First, I will present our work on theoretically understanding FL training convergence and generalization using a neural tangent kernel, called FL-NTK. Then, I will present our algorithms for tackling data heterogeneity (on features and labels) and device heterogeneity, motivated by our previous theoretical foundation. Lastly, I will also show the promising results of applying our FL algorithms in real-world applications.

Biography:

Dr. Xiaoxiao Li is an Assistant Professor at the Department of Electrical and Computer Engineering at The University of British Columbia (UBC) starting August 2021. In addition, Dr. Li is an adjunct Assistant Professor at Yale University. Before joining UBC, Dr. Li was a Postdoc Research Fellow at Princeton University. Dr. Li obtained her Ph.D. degree from Yale University in 2020. Dr. Li's research focuses on developing theoretical and practical solutions for enhancing the trustworthiness of AI systems in healthcare. Specifically, her recent research has been dedicated to advancing federated learning techniques and their applications in the medical field. Dr. Li's work has been recognized with numerous publications in top-tier machine learning conferences and journals, including NeurIPS, ICML, ICLR, MICCAI, IPMI, ECCV, TMI, TNNLS, Medical Image Analysis, and Nature Methods.