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cs.LG2026
Dimension-Free Private Mean Estimation is Impossible Without the Covariance
Renaud Gaucher, Ephraim Linder, Adam Smith +2
We study the limits of differentially private Gaussian mean estimation when the covariance matrix is unknown. Prior work shows that when the covariance is known, the optimal sa…
cs.LG2026
From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity
Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx
Standard federated learning algorithms are vulnerable to adversarial nodes, a.k.a. Byzantine failures. To solve this issue, robust distributed learning algorithms have been develop…
cs.LG2024
Byzantine-Robust Gossip: Insights from a Dual Approach
Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx
Distributed learning has many computational benefits but is vulnerable to attacks from a subset of devices transmitting incorrect information. This paper investigates Byzantine-res…