2 papers
cs.LG2025
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…
cs.LG2025
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…