LANS Seminar

Seminar Title: Privacy-Preserving Distributed Optimization Algorithms and Software Framework
Speaker: Minseok Ryu, Postdoctoral Appointee, ANL/MCS

Date/Time: December 14, 2022 / 10:30 AM – 11:30 AM
Location: See meeting URL on the cels-seminars website (requires Argonne login)

Host: Kibaek Kim

Description: In distributed optimization (DO), multiple agents cooperate to minimize a global objective function, which can be expressed as a sum of local objective functions, subject to some constraints. Specifically, each agent solves a local optimization model constructed by its own data as well as the information received from its neighbors in a communication network. Compared with a centralized framework, DO has several advantages, such as (i) less communication as the agents share limited amounts of information, (ii) robustness with respect to the failure of individual agents, and (iii) the potential to be computationally superior mainly due to parallel computations. Due to these advantages, DO has been utilized in various application areas, such as power systems and machine learning. However, most DO algorithms do not guarantee data privacy even though data are not shared with others, which limits the practical usage of DO in applications with sensitive personal data. To address the data privacy concern in DO, we propose privacy-preserving DO algorithms that provide a statistical guarantee of data privacy known as differential privacy and apply them to some optimization problems in power systems as well as federated learning (FL). The proposed privacy-preserving DO algorithms stand out in that a sequence of iterates converges to an optimal solution in expectation without any error bound. Additionally, we develop and release an open-source software framework for privacy-preserving FL which enables real-world FL by communication information via gRPC as well as simulation of FL under an HPC architecture.

Bio:  Minseok Ryu is a postdoctoral appointee in MCS division at Argonne. He received his Ph.D. from the University of Michigan, majoring Industrial and Operations Engineering. His research interests lie broadly in machine learning and operations research, especially in advancing distributed optimization algorithms, addressing computational challenges in decision-making under uncertainty, and their applications for the advancement of science and social goods, including but not limited to energy and healthcare systems.

Please note that the meeting URL for this event can be seen on the cels-seminars website, which requires an Argonne login.