3 papers
cs.CR2026
Ball Differential Privacy: How to Mitigate Data Reconstruction with Less Noise
Joseph Margaryan, Nirupam Gupta
Vector embeddings of raw records, while not human-readable, do not preserve record privacy: an adversary can reconstruct training records from a released model even when that model…
cs.LG2026
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…
cs.LG2025
Reconciling Communication Compression and Byzantine-Robustness in Distributed Learning
Diksha Gupta, Antonio Honsell, Chuan Xu +2
Distributed learning enables scalable model training over decentralized data, but remains hindered by Byzantine faults and high communication costs. While both challenges have been…