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cs.LG2026
Disparate Impact in Synthetic Data Generation
Paul Andrey, Michaël Perrot, Batiste Le Bars +1
We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups.…
cs.LG2026
Loss Gap Parity for Fairness in Heterogeneous Federated Learning
Brahim Erraji, Michaël Perrot, Aurélien Bellet
While clients may join federated learning to improve performance on data they rarely observe locally, they often remain self-interested, expecting the global model to perform well…
cs.LG2026
Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
Jean Dufraiche, Paul Mangold, Michaël Perrot +1
Releasing data once and for all under noninteractive Local Differential Privacy (LDP) enables complete data reusability, but the resulting noise may create bias in subsequent analy…