4 papers
Federated Learning with Heterogeneous and Private Label Sets
Adam Breitholtz, Edvin Listo Zec, Fredrik D. Johansson
Although common in real-world applications, heterogeneous client label sets are rarely investigated in federated learning (FL). Furthermore, in the cases they are, clients are assu…
Overcoming label shift with target-aware federated learning
Edvin Listo Zec, Adam Breitholtz, Fredrik D. Johansson
Federated learning enables multiple actors to collaboratively train models without sharing private data. Existing algorithms are successful and well-justified in this task when the…
On the effects of similarity metrics in decentralized deep learning under distributional shift
Edvin Listo Zec, Tom Hagander, Eric Ihre-Thomason +1
Decentralized Learning (DL) enables privacy-preserving collaboration among organizations or users to enhance the performance of local deep learning models. However, model aggregati…
Impacts of Color and Texture Distortions on Earth Observation Data in Deep Learning
Martin Willbo, Aleksis Pirinen, John Martinsson +3
Land cover classification and change detection are two important applications of remote sensing and Earth observation (EO) that have benefited greatly from the advances of deep lea…