23 citations · 25 across the 11 of their papers we have counts for
5 papers · 1 filter
Seamless Integration: Sampling Strategies in Federated Learning Systems
Tatjana Legler, Vinit Hegiste, Martin Ruskowski
Federated Learning (FL) represents a paradigm shift in the field of machine learning, offering an approach for a decentralized training of models across a multitude of devices whil…
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment
Tatjana Legler, Vinit Hegiste, Ahmed Anwar +1
Federated learning (FL) has emerged as a promising approach to training machine learning models across decentralized data sources while preserving data privacy, particularly in man…
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data
Ahmed Anwar, Brian Moser, Dayananda Herurkar +4
The emergence of federated learning (FL) presents a promising approach to leverage decentralized data while preserving privacy. Furthermore, the combination of FL and anomaly detec…
Federated Object Detection for Quality Inspection in Shared Production
Vinit Hegiste, Tatjana Legler, Martin Ruskowski
Federated learning (FL) has emerged as a promising approach for training machine learning models on decentralized data without compromising data privacy. In this paper, we propose…
Application of federated learning in manufacturing
Vinit Hegiste, Tatjana Legler, Martin Ruskowski
A vast amount of data is created every minute, both in the private sector and industry. Whereas it is often easy to get hold of data in the private entertainment sector, in the ind…