5 papers
Adaptive Federated Learning via Dynamical System Model
Aayushya Agarwal, Larry Pileggi, Gauri Joshi
Hyperparameter selection is critical for stable and efficient convergence of heterogeneous federated learning, where clients differ in computational capabilities, and data distribu…
Integrating Forecasting Models Within Steady-State Analysis and Optimization
Aayushya Agarwal, Larry Pileggi
Extreme weather variations and the increasing unpredictability of load behavior make it difficult to determine power grid dispatches that are robust to uncertainties. While machine…
A Hybrid Simulation of DNN-based Gray Box Models
Aayushya Agarwal, Yihan Ruan, Larry Pileggi
Simulation is vital for engineering disciplines, as it enables the prediction and design of physical systems. However, the computational challenges inherent to large-scale simulati…
FedECADO: A Dynamical System Model of Federated Learning
Aayushya Agarwal, Gauri Joshi, Larry Pileggi
Federated learning harnesses the power of distributed optimization to train a unified machine learning model across separate clients. However, heterogeneous data distributions and…
Second-Order Optimization via Quiescence
Aayushya Agarwal, Larry Pileggi, Ronald Rohrer
Second-order optimization methods exhibit fast convergence to critical points, however, in nonconvex optimization, these methods often require restrictive step-sizes to ensure a mo…