26 citations · 82 across the 9 of their papers we have counts for
3 papers · 1 filter
Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization
Vassilios Yfantis, Achim Wagner, Martin Ruskowski
The use of distributed optimization in machine learning can be motivated either by the resulting preservation of privacy or the increase in computational efficiency. On the one han…
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…