11 citations · 32 across the 5 of their papers we have counts for
6 papers
From Pilots to Production: Lessons in Cross-Institutional Federated Training and Artificial Intelligence for Science
Olivera Kotevska, Max Carlson, Yan Gao +10
Many of the most valuable scientific datasets cannot be centralized: they are proprietary, export-controlled, classified, or bound by data-sovereignty restrictions. This inverts th…
Predictive Energy Management for Battery Electric Vehicles with Hybrid Models
Yu-Wen Huang, Christian Prehofer, William Lindskog +3
This paper addresses the problem of predicting the energy consumption for the drivers of Battery electric vehicles (BEVs). Several external factors (e.g., weather) are shown to hav…
Federated Learning for Drowsiness Detection in Connected Vehicles
William Lindskog, Valentin Spannagl, Christian Prehofer
Ensuring driver readiness poses challenges, yet driver monitoring systems can assist in determining the driver's state. By observing visual cues, such systems recognize various beh…
A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks
William Lindskog, Christian Prehofer
Federated Learning (FL) has lately gained traction as it addresses how machine learning models train on distributed datasets. FL was designed for parametric models, namely Deep Neu…
Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data
William Lindskog, Christian Prehofer, Sarandeep Singh
Federated Learning (FL) has gained considerable traction, yet, for tabular data, FL has received less attention. Most FL research has focused on Neural Networks while Tree-Based Mo…
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
William Lindskog, Christian Prehofer
In this paper, we show how Federated Learning (FL) can be applied to vehicular use-cases in which we seek to classify obstacles, irregularities and pavement types on roads. Our pro…