6 papers
Budget-Constrained Graph Augmentation for Robust Network Design via Kirchhoff Index Minimization
Omkar Bhoite, V Sateeshkrishna Dhuli, Stefan Werner +1
Enhancing the robustness of deployed networks against failures and disruptions is critical for reliable operation. This requires deciding which new links to install and how strongl…
AdGT: Decentralized Gradient Tracking with Adaptive Per-Agent Stepsizes
Diyako Ghaderyan, Stefan Werner
In decentralized optimization, gradient-tracking methods typically rely on a single global stepsize. This choice can be conservative when agents have local objectives with differen…
Federated Smoothing ADMM for Localization
Reza Mirzaeifard, Ashkan Moradi, Masahiro Yukawa +1
This paper addresses the challenge of localization in federated settings, which are characterized by distributed data, non-convexity, and non-smoothness. To tackle the scalability…
Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning
Reza Mirzaeifard, Stefan Werner
This paper presents a hierarchical federated learning (FL) framework that extends the alternating direction method of multipliers (ADMM) with smoothing techniques, tailored for non…
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…