Cover Image

DP-SGD Without Clipping: The Lipschitz Neural Network Way

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bethune, Louis, Massena, Thomas, Boissin, Thibaut, Prudent, Yannick, Friedrich, Corentin, Mamalet, Franck, Bellet, Aurelien, Serrurier, Mathieu, Vigouroux, David
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
by: Serrurier, Mathieu, et al.
Published: (2022)

Turbo-Muon: Accelerating Orthogonality-Based Optimization with Pre-Conditioning
by: Boissin, Thibaut, et al.
Published: (2025)

Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks
by: Massena, Thomas, et al.
Published: (2025)

Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
by: Cebere, Tudor, et al.
Published: (2024)

Orthogonium : A Unified, Efficient Library of Orthogonal and 1-Lipschitz Building Blocks
by: Boissin, Thibaut, et al.
Published: (2026)

Fast and Flexible Robustness Certificates for Semantic Segmentation
by: Massena, Thomas, et al.
Published: (2025)

On the Convergence of DP-SGD with Adaptive Clipping
by: Shulgin, Egor, et al.
Published: (2024)

From SGD to Muon: Adaptive Optimization via Schatten-p Norms
by: Massena, Thomas, et al.
Published: (2026)

An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures
by: Boissin, Thibaut, et al.
Published: (2025)

Privacy Auditing with Zero (0) Training Run
by: Cebere, Tudor, et al.
Published: (2026)

Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
by: Zhang, Xinwei, et al.
Published: (2023)

DP-SGD with weight clipping
by: Barczewski, Antoine, et al.
Published: (2023)

To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)

GeoClip: Geometry-Aware Clipping for Differentially Private SGD
by: Gilani, Atefeh, et al.
Published: (2025)

FlashDP: Private Training Large Language Models with Efficient DP-SGD
by: Wang, Liangyu, et al.
Published: (2025)

R+R:Understanding Hyperparameter Effects in DP-SGD
by: Morsbach, Felix, et al.
Published: (2024)

Differentially Private Decentralized Learning with Random Walks
by: Cyffers, Edwige, et al.
Published: (2024)

Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms
by: Ganesh, Arun
Published: (2024)

Rethinking the Security of DP-SGD: A Corrected Analysis of Differentially Private Machine Learning
by: Wang, Wenhao, et al.
Published: (2026)

Clipped SGD Algorithms for Performative Prediction: Tight Bounds for Clipping Bias and Remedies
by: Li, Qiang, et al.
Published: (2024)

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
by: Ertan, Murat Bilgehan, et al.
Published: (2026)

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
by: Cyffers, Edwige, et al.
Published: (2022)

Privacy Attacks in Decentralized Learning
by: Mrini, Abdellah El, et al.
Published: (2024)

Balls-and-Bins Sampling for DP-SGD
by: Chua, Lynn, et al.
Published: (2024)

How Private are DP-SGD Implementations?
by: Chua, Lynn, et al.
Published: (2024)

Privacy in Theory, Bugs in Practice: Grey-Box Auditing of Differential Privacy Libraries
by: Cebere, Tudor, et al.
Published: (2026)

Closed-Form Bounds for DP-SGD against Record-level Inference
by: Cherubin, Giovanni, et al.
Published: (2024)

Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
by: Bethune, Louis, et al.
Published: (2023)

Privacy Enhanced PEFT: Tensor Train Decomposition Improves Privacy Utility Tradeoffs under DP-SGD
by: Kunwar, Pradip, et al.
Published: (2026)

DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
by: Wei, Chengkun, et al.
Published: (2025)

The Relative Gaussian Mechanism and its Application to Private Gradient Descent
by: Hendrikx, Hadrien, et al.
Published: (2023)

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
by: Pierquin, Clément, et al.
Published: (2023)

Privacy Amplification Through Synthetic Data: Insights from Linear Regression
by: Pierquin, Clément, et al.
Published: (2025)

Nebula: Efficient, Private and Accurate Histogram Estimation
by: Shamsabadi, Ali Shahin, et al.
Published: (2024)

Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?
by: Dong, Andy, et al.
Published: (2026)

Lap2: Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory
by: Mohammady, Meisam, et al.
Published: (2026)

It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With Non-Convex Loss
by: Annamalai, Meenatchi Sundaram Muthu Selva
Published: (2024)

Have it your way: Individualized Privacy Assignment for DP-SGD
by: Boenisch, Franziska, et al.
Published: (2023)

Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
by: Thudi, Anvith, et al.
Published: (2023)

DP-SGD-Global-Adapt-V2-S: Triad Improvements of Privacy, Accuracy and Fairness via Step Decay Noise Multiplier and Step Decay Upper Clipping Threshold
by: Chilukoti, Sai Venkatesh, et al.
Published: (2023)