9 citations · 31 across the 31 of their papers we have counts for
37 papers
A Locally Penalized Cross-Estimate Federated Method with Guarantees for Constrained Personalized Learning
Mohammadjavad Ebrahimi, Daniel Burbano, Farzad Yousefian
Federated learning (FL) is a communication-efficient framework for solving distributed optimization problems. Standard FL methods aggregate local updates into a single global model…
Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation
Fateme Maleki, Krishnan Raghavan, Farzad Yousefian
Federated learning (FL) is a communication-efficient collaborative learning framework that enables model training across multiple agents with private local datasets. While the bene…
On the Sampling-based Computation of Nash Equilibria under Uncertainty via the Nikaido-Isoda Function
Luke Marrinan, Farzad Yousefian, Uday V. Shanbhag
We consider the computation of an equilibrium of a stochastic Nash equilibrium problem, where the player objectives are assumed to be -Lipschitz continuous and convex given ri…
On the Resolution of Stochastic MPECs over Networks: Distributed Implicit Zeroth-Order Gradient Tracking Methods
Mohammadjavad Ebrahimi, Uday V. Shanbhag, Farzad Yousefian
The mathematical program with equilibrium constraints (MPEC) is a powerful yet challenging class of constrained optimization problems, where the constraints are characterized by a…
On iteratively regularized first-order methods for simple bilevel optimization
Sepideh Samadi, Daniel Burbano, Farzad Yousefian
We consider simple bilevel optimization (SBO) problems where the goal is to compute among the optimal solutions of a composite convex optimization problem, one that minimizes a sec…
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…