4 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
Federated Smoothing Proximal Gradient for Quantile Regression with Non-Convex Penalties
Reza Mirzaeifard, Diyako Ghaderyan, Stefan Werner
Distributed sensors in the internet-of-things (IoT) generate vast amounts of sparse data. Analyzing this high-dimensional data and identifying relevant predictors pose substantial…
Decentralized Smoothing ADMM for Quantile Regression with Non-Convex Sparse Penalties
Reza Mirzaeifard, Diyako Ghaderyan, Stefan Werner
In the rapidly evolving internet-of-things (IoT) ecosystem, effective data analysis techniques are crucial for handling distributed data generated by sensors. Addressing the limita…
Robust Networked Federated Learning for Localization
Reza Mirzaeifard, Naveen K. D. Venkategowda, Stefan Werner
This paper addresses the problem of localization, which is inherently non-convex and non-smooth in a federated setting where the data is distributed across a multitude of devices.…
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs
Stefan Werner, Sebastian Peitz
The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by part…