23 citations · 26 across the 11 of their papers we have counts for
11 papers
Sustainable Transfer Learning for Adaptive Robot Skills
Khalil Abuibaid, Vinit Hegiste, Nigora Gafur +2
Learning robot skills from scratch is often time-consuming, while reusing data promotes sustainability and improves sample efficiency. This study investigates policy transfer acros…
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…
Enhancing Object Detection Performance for Small Objects through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios
Jibinraj Antony, Vinit Hegiste, Ali Nazeri +4
Object Detection (OD) has proven to be a significant computer vision method in extracting localized class information and has multiple applications in the industry. Although many o…
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…