6 papers · 1 filter
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…
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.…
Structure Learning of Sparse GGMs over Multiple Access Networks
Mostafa Tavassolipour, Armin Karamzade, Reza Mirzaeifard +2
A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple local machines. The loca…