Seminar| Institute of Mathematical Sciences
Time: Friday, July 3th, 2026,15:30-16:30
Location: IMS R518
Speaker: Linglong Kong, University of Alberta
Abstract:Traditional static federated learning in distributed environments is facing new challenges, with data streaming and privacy emerging as significant concerns. This paper addresses the problem of online statistical inference in a federated learning framework with an untrusted central server, adhering to privacy constraints. Under the rigorous local differential privacy constraint, we consider a general optimizationbased framework with streaming data. We introduce a noisy local stochastic gradient descent algorithm and its variants to study synchronous and asynchronous federated learning scenarios without and with delays in local updates, respectively. Our proposed algorithms are single-pass, depending only on the current data and the previous estimate, which effectively reduces both time and space complexity. We also establish the convergence rates and functional central limit theorems for the proposed algorithms, providing theoretical justification for our online inference tools. Specifically, for both scenarios, we present two online inference methods: private plug-in and random scaling, to construct private confidence intervals efficiently in an online manner. Our numerical analyses assess the performance of the proposed methods across various conditions, including time heterogeneity, sample size, and privacy budget, recommending settings where each approach excels.
About the Speaker: Dr. Linglong Kong is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, holding a Canada Research Chair in Statistical Learning and a Canada CIFAR AI Chair. He is a Fellow of the American Statistical Association (ASA) and the Alberta Machine Intelligence Institute (Amii), with over 150 peer-reviewed publications in leading journals and conferences such as AOS, JASA, JRSSB, NeurIPS, ICML, and ICLR. Dr. Kong received the 2025 CRM-SSC Prize for outstanding research in Canada. He serves as Associate Editor for several top journals, including JASA and AOAS, and has held leadership roles within the ASA and the Statistical Society of Canada. Dr. Kong’s research interests include high-dimensional and neuroimaging data analysis, statistical machine learning, robust statistics, quantile regression, trustworthy machine learning, and artificial intelligence for smart health.