Theory-driven Federated Learning Algorithms for Heterogeneous Data

Xiaoxiao Li - University of British Columbia

Nov. 11, 2022, 2:30 p.m. - Nov. 11, 2022, 3:30 p.m.

McConnell 11

Hosted by: Yue Li

Abstract: 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 multi-institutional data analysis in the FL setting.  I will present our algorithms for tackling feature and label heterogeneity, motivated by our previous theoretical foundation. I will also show the promising results of applying our FL algorithms in healthcare applications.



Bio: Dr. Xiaoxiao Li is an Assistant Professor at the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC) starting August 2021. Before joining UBC, Dr. Li was a Postdoc Research Fellow in the Computer Science Department at Princeton University. Dr. Li obtained her PhD degree from Yale University in 2020. Dr. Li’s research interests range across the interdisciplinary fields of deep learning and biomedical data analysis, aiming to improve the trustworthiness of AI systems for healthcare. Dr. Li has had over 30 papers published in leading machine learning conferences and journals, including NeurIPS, ICML, ICLR, MICCAI, IPMI, ECCV, IEEE Transactions on Medical Imaging, and Medical Image Analysis. Her work has been recognized with several best paper awards at international conferences.