Speaker: Dr. Quoc Tran-Dinh from the University of North Carolina at Chapel Hill
Date and Time: Thursday, November 11, 2021, 10am-11am via Zoom. Please contact Qingning Zhou to obtain the Zoom link.
Title: Randomized Douglas-Rachford Splitting Algorithms for Composite Optimization in Federated Learning
Abstract: In this talk, we present two randomized Douglas-Rachford splitting algorithms to solve a class of composite nonconvex finite-sum optimization problems arising from federated learning. Our algorithms rely on a combination of three main techniques: Douglas-Rachford splitting scheme, randomized block-coordinate technique, and asynchronous strategy. We show that our algorithms achieve the best-known communication complexity bounds under standard assumptions in the nonconvex setting, while allow one to inexactly updating local models with only a subset of users each round, and handle nonsmooth convex regularizers. Our second algorithm can be implemented in an asynchronous mode using a general probabilistic model to capture different computational architectures. We illustrate our algorithms with many numerical examples and show that the new algorithms have a promising performance compared to common existing methods. This talk is based on the collaboration with Nhan Pham (UNC), Lam M. Nguyen (IBM), and Dzung Phan (IBM). Our paper is available at: https://arxiv.org/abs/2103.03452.