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McKelvey School of Engineering

Brown School

ESE Seminar: Jin Sima

Thursday, February 8 | 11:00 AM - 12:00 PM

Preston M. Green Hall, Rodin Auditorium, L0120
135 N Skinker Blvd, St. Louis, MO 63112, USA

Data privacy and coding for machine learning: From federated learning to differentially private online learning

Abstract: With the widespread availability of personal data to machine learning models and the adoption of a multitude of data protection regulations, data privacy has become one of the key concerns in designing machine learning systems.  I will discuss different aspects and notions of data privacy that are suitable for different types of data and learning purposes. First, I will discuss unlearning of clustering models in federated settings, where data privacy is required in two ways:  within the unlearning paradigm that involves the removal of training data points as well as their influence on the learning model and the federated learning paradigm that is concerned with aggregating locally trained models without leaking information. I will present a federated framework that integrates a computation-efficient unlearning mechanism and a communication-efficient algorithm for secure aggregation of sparse local models based on novel ideas borrowed from coding theory. Second, I will discuss classification in hyperbolic spaces in a federated setting and present an algorithm that involves secure aggregation of local models under the presence of label-switching issues that are common in federated settings. Label switching is resolved via the use of Bh codes. If time permits, I will also discuss a recent problem in differentially private online learning.

Event Type



McKelvey School of Engineering


Science & Technology

Electrical & Systems Engineering


Event Contact

Aaron Beagle | abeagle@wustl.edu

Speaker Information

Bio: Jin Sima is a postdoctoral researcher in the Department of electrical and computer engineering at University of Illinois Urbana-Champaign. He received a B.Eng. and a M.Sc. in electronic engineering from Tsinghua University, China, in 2013 and 2016 respectively, and a Ph.D in electrical engineering from California Institute of Technology (Caltech) in 2022. His research interests include information and coding theory, machine learning, and theory of computation. He is a recipient of the 2019 IEEE Jack Keil Wolf ISIT Student Paper Award, the 2020-2021 IEEE Communication Society Data Storage Best Paper Award, the 2022 Caltech Charles Wilts Prize for best doctoral thesis, and the 2023 Thomas M. Cover Dissertation Award.

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