2 papers
astro-ph.IM2026
Unsupervised Domain Adaptation for Multitask Image Analysis in Realistic Context with Extreme Label Shift; Application to the CTAO first Large Sized Telescope
Michaël Dell'aiera, Thomas Vuillaume, Alexandre Benoit
Unsupervised domain adaptation is a widespread set of methods that leverages the knowledge of a labeled source domain to train a model to perform well on a related unlabeled target…
cs.CR2026
From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning
Huong Nguyen, Mickaël Bettinelli, Amirhossein Ghaffari +4
Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning. This paradigm enables privacy with multiple clients to train a shared-task mode…