2 papers
cs.LG2025
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays
Charikleia Iakovidou, Kibaek Kim
Federated learning (FL) was recently proposed to securely train models with data held over multiple locations (``clients'') under the coordination of a central server. Prolonged tr…
math.OC2025
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…