2 papers
cs.LG2025
FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection
Gergely D. Németh, Eros Fanì, Yeat Jeng Ng +4
Federated Learning (FL) enables decentralized training of machine learning models on distributed data while preserving privacy. However, in real-world FL settings, client data is o…
cs.LG2025
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning
Gergely Dániel Németh, Miguel Ãngel Lozano, Novi Quadrianto +1
Federated Learning (FL) has been proposed as a privacy-preserving solution for distributed machine learning, particularly in heterogeneous FL settings where clients have varying co…