23 citations · 25 across the 11 of their papers we have counts for
11 papers
The Power of Light: Improving Synthetic-to-Real Domain Adaptation through Physically-Based Indirect Illumination
Hooman Tavakoli Ghinani, Tatjana Legler, Martin Ruskowski
While synthetic data generation resolves the manual labeling bottleneck in computer vision, minimizing the syn-to-real domain gap requires optimizing rendering variables. This pape…
OPC UA Shared-Memory: Conceptual Elaboration and Prototypical Implementation Using Iceoryx2
Thomas Barth, Tatjana Legler, Achim Wagner +1
The increasing virtualization of automation software leads to a growing co-location of heterogeneous applications on shared edge devices and fosters modern microservice architectur…
Federated Action Recognition for Smart Worker Assistance Using FastPose
Vinit Hegiste, Vidit Goyal, Tatjana Legler +1
In smart manufacturing environments, accurate and real-time recognition of worker actions is essential for productivity, safety, and human-machine collaboration. While skeleton-bas…
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…
Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques
Vinit Hegiste, Snehal Walunj, Jibinraj Antony +2
Federated Learning (FL) has garnered significant attention in manufacturing for its robust model development and privacy-preserving capabilities. This paper contributes to research…
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…
Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing
Vinit Hegiste, Tatjana Legler, Martin Ruskowski
In the realm of Federated Learning (FL), particularly within the manufacturing sector, the strategy for selecting client weights for server aggregation is pivotal for model perform…
Federated Ensemble YOLOv5 -- A Better Generalized Object Detection Algorithm
Vinit Hegiste, Tatjana Legler, Martin Ruskowski
Federated learning (FL) has gained significant traction as a privacy-preserving algorithm, but the underlying resemblances of federated learning algorithms like Federated averaging…
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…