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cs.LG2026
Rashomon Sets and Model Multiplicity in Federated Learning
Xenia Heilmann, Luca Corbucci, Mattia Cerrato
The Rashomon set captures the collection of models that achieve near-identical empirical performance yet may differ substantially in their decision boundaries. Understanding the di…
cs.LG2025
FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
Xenia Heilmann, Luca Corbucci, Mattia Cerrato +1
Federated Learning (FL) enables collaborative training while preserving privacy, yet it introduces a critical challenge: the "illusion of fairness''. A global model, usually evalua…