4 papers
A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning
Yuyang Qiu, Kibaek Kim, Farzad Yousefian
Heterogeneity in federated learning (FL) is a critical and challenging aspect that significantly impacts model performance and convergence. In this paper, we propose a novel framew…
Self-tuned Regularized Federated Methods with Guarantees for Optimal Solution Selection
Mohammadjavad Ebrahimi, Yuyang Qiu, Shisheng Cui +1
We study a hierarchical federated learning (FL) problem, where clients cooperatively seek to select among multiple optimal solutions of a primary distributed learning problem, a so…
Iteratively Regularized Gradient Tracking Methods for Optimal Equilibrium Seeking
Yuyang Qiu, Farzad Yousefian, Brian Zhang
In noncooperative Nash games, equilibria are often inefficient. This is exemplified by the Prisoner's Dilemma and was first provably shown in the 1980s. Since then, understanding t…
Zeroth-Order Federated Methods for Stochastic MPECs and Nondifferentiable Nonconvex Hierarchical Optimization
Yuyang Qiu, Uday V. Shanbhag, Farzad Yousefian
Motivated by the emergence of federated learning (FL), we design and analyze federated methods for addressing: (i) Nondifferentiable nonconvex optimization; (ii) Bilevel optimizati…