Zobrazeno 1 - 10
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pro vyhledávání: '"Legler, Tatjana"'
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 performance. This study investigates the comparative effectiveness of two wei
Externí odkaz:
http://arxiv.org/abs/2408.10024
Federated learning (FL) has emerged as a promising approach to training machine learning models across decentralized data sources while preserving data privacy, particularly in manufacturing and shared production environments. However, the presence o
Externí odkaz:
http://arxiv.org/abs/2408.09556
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 while maintaining the privacy of local data. However, the dynamic nature o
Externí odkaz:
http://arxiv.org/abs/2408.09545
Federated Learning (FL) has garnered significant attention in manufacturing for its robust model development and privacy-preserving capabilities. This paper contributes to research focused on the robustness of FL models in object detection, hereby pr
Externí odkaz:
http://arxiv.org/abs/2408.08974
Autor:
Anwar, Ahmed, Moser, Brian, Herurkar, Dayananda, Raue, Federico, Hegiste, Vinit, Legler, Tatjana, Dengel, Andreas
The emergence of federated learning (FL) presents a promising approach to leverage decentralized data while preserving privacy. Furthermore, the combination of FL and anomaly detection is particularly compelling because it allows for detecting rare a
Externí odkaz:
http://arxiv.org/abs/2408.04442
Federated learning (FL) has gained significant traction as a privacy-preserving algorithm, but the underlying resemblances of federated learning algorithms like Federated averaging (FedAvg) or Federated SGD (Fed SGD) to ensemble learning algorithms h
Externí odkaz:
http://arxiv.org/abs/2306.17829
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 a FL algorithm for object detection in quality inspection tasks using
Externí odkaz:
http://arxiv.org/abs/2306.17645
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 industrial production environment it is much more difficult due to laws,
Externí odkaz:
http://arxiv.org/abs/2208.04664
Publikováno v:
In Procedia Manufacturing 2020 51:416-423
Publikováno v:
KI: Künstliche Intelligenz; Jun2019, Vol. 33 Issue 2, p111-116, 6p