4 papers
Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness
Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1
Recent work has established a fundamental trilemma between Byzantine robustness, local differential privacy (LDP), and optimization error in distributed learning. We show that this…
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.…
Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning
Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1
Robust distributed learning algorithms aim to maintain reliable performance despite the presence of misbehaving workers. Such misbehaviors are commonly modeled as \textit{Byzantine…
Marginal and training-conditional guarantees in one-shot federated conformal prediction
Pierre Humbert, Batiste Le Bars, Aurélien Bellet +1
We study conformal prediction in the one-shot federated learning setting. The main goal is to compute marginally and training-conditionally valid prediction sets, at the server-lev…