Summary

Tatjana Legler studied mechanical engineering at the Technical University of Kaiserslautern. She wrote her master thesis on "Optimization of automated visual inspection of common rails using neural networks". She has been working as a researcher at the Chair of Machine Tools and Control Systems since November 2017.

Research Fields

Tatjana Legler deals with the use of artificial intelligence in the production environment. This includes, for example, the analysis of process data for the prediction of product quality and federated learning.

Publications

T. Legler, V. Hegiste, A. Anwar and M. Ruskowski, Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment, 2025. Procedia Computer Science, 253, pp. 2831–2840. Link

T. Legler, V. Hegiste and M. Ruskowski, Benchmarking Federated Learning Under Realistic Non-IID Conditions. A Structured Partitioning Approach Using ImageNet, 2025.

A. Anwar, B. Moser, D. Herurkar, F. Raue, V. Hegiste, T. Legler and A. Dengel, FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data, 2024. 2nd International Conference on Federated Learning Technologies and Applications (FLTA), Valencia, Spain, pp. 115–122. IEEE.

V. Hegiste, T. Legler and M. Ruskowski, Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing, 2024. 2nd International Conference on Federated Learning Technologies and Applications (FLTA), Valencia, Spain, pp. 55–62. IEEE.

V. Hegiste, S. Walunj, J. Antony, T. Legler and M. Ruskowski, Enhancing Object Detection with Hybrid Dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques, 2024. 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR), Muscat, Oman, pp. 1–6. IEEE.

T. Legler, V. Hegiste and M. Ruskowski, Seamless Integration: Sampling Strategies in Federated Learning Systems, 2024. 2nd International Conference on Federated Learning Technologies and Applications (FLTA), Valencia, Spain, pp. 148–155. IEEE.

V. Hegiste, T. Legler, K. Fridman and M. Ruskowski, Federated Object Detection for Quality Inspection in Shared Production, 2023. Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Tartu, Estonia, pp. 151–158. IEEE.

V. Hegiste, T. Legler and M. Ruskowski, Federated Ensemble YOLOv5 – A Better Generalized Object Detection Algorithm, 2023. Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Tartu, Estonia, pp. 7–14. IEEE.

T. Legler, V. Hegiste and M. Ruskowski, Mapping of Newcomer Clients in Federated Learning Based on Activation Strength, 2023. 32nd International Conference on Flexible Automation and Intelligent Manufacturing.

V. Hegiste, T. Legler and M. Ruskowski, Application of Federated Machine Learning in Manufacturing, 2022. International Conference on Industry 4.0 Technology (I4Tech), Pune, India, pp. 1–8. IEEE. Link

M. Volkmann, T. Legler, A. Wagner and M. Ruskowski, A CAD Feature-Based Manufacturing Approach with OPC UA Skills, 2020. Procedia Manufacturing, 51, pp. 416–423. Link