18 citations · 22 across the 4 of their papers we have counts for
3 papers · 1 filter
Reconsidering Fairness Through Unawareness From the Perspective of Model Multiplicity
Benedikt Höltgen, Nuria Oliver
Fairness through Unawareness (FtU) describes the idea that discrimination against demographic groups can be avoided by not considering group membership in the decisions or predicti…
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…
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning
Gergely Dániel Németh, Miguel Ángel 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…