79 citations · 220 across the 50 of their papers we have counts for
13 papers · 1 filter
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…
Dangerous Liaisons of Convex Learning and Non-Affine Aggregation
Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1
Last-iterate convergence and generalization guarantees in first-order convex learning hinge on the monotonicity of the update operator. While linear averaging preserves the monoton…
Detectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run
Mathieu Dagréou, Aurélien Bellet
Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bounds on differential privacy…
Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees
Christian Janos Lebeda, David Erb, Tudor Cebere +1
Random forests are widely used in fields involving sensitive tabular data, but existing approaches to enforcing differential privacy (DP) typically degrade performance to the point…
Privacy Auditing with Zero (0) Training Run
Tudor Cebere, Mathieu Even, Linus Bleistein +1
Privacy auditing provides empirical lower bounds on the differential privacy parameters of learning algorithms. Existing methods, however, require interventional access to the trai…
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…