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Michele Fontana

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Open access Aug 2026

Federated learning with multiple, intersectional and multiclass fairness guarantees under performance budgets

Machine learning increasingly drives decisions in domains such as finance and healthcare, where ethical considerations, such as fairness, are central. In such contexts, ensuring fairness is essential, especially when decisions impact individuals and social groups. Federated learning (FL) provides a decentralized traini...

Michele Fontana, Francesca Naretto, A. Monreale · Data mining and knowledge di... · 0 citations

#federated learning Open access Aug 2026

Federated learning with multiple, intersectional and multiclass fairness guarantees under performance budgets

FedFairLAB is introduced, a FL method that enforces group, intersectional, and multiclass fairness simultaneously at both the local and global levels and a tunable performance budget allows practitioners to control how much predictive performance can be sacrificed to improve fairness.

Michele Fontana, Francesca Naretto, A. Monreale · Data mining and knowledge di... · 0 citations