DP-MacAdam: Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum

• URL: http://arxiv.org/abs/2606.05435v1
• Date: Wed, 03 Jun 2026 20:58:34 GMT
• Title: DP-MacAdam: Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum
• Abstract summary: Differentially private gradient descent (DP-SGD) has become the standard framework for privacy-preserving machine learning.<n>We propose Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum (DP-MacAdam)
• Score: 13.705460306140004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differentially private stochastic gradient descent (DP-SGD) has become the standard framework for privacy-preserving machine learning, yet its reliance on a fixed gradient clipping threshold to limit sensitivity remains a significant practical limitation. Adaptive clipping algorithms such as AdaClip shift and scale the gradient prior to clipping and adding noise so that the clipped gradient yields a more informative descent direction. The shift and scaling parameters are selected adaptively based on the empirical mean and variance. However, in existing adaptive clipping algorithms, these empirical estimates have not been also used for momentum to accelerate training itself. On the other hand, DP-Adam is an algorithm that exploits Adam-like momentum updates based on the gradient mean and variance to accelerate training, but does not exploit these estimates for adaptive clipping. In this work, we propose Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum (DP-MacAdam), a novel algorithm that combines these two approaches so as to use the same mean and variance estimates for both clipping and momentum. We perform an analysis showing that DP-MacAdam estimates the gradient variances in a bias-free manner. In addition, we empirically evaluate the privacy and accuracy of DP-MacAdam, demonstrating that it achieves improved model utility compared to DP-SGD, AdaClip, and DP-Adam baselines, without requiring manual tuning of the clipping threshold.

Related papers

• DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models [25.94292199187493]
  Federated learning (FL) enables the collaborative training of large-scale language models across edge devices while keeping user data on-device.<n>DP-SGD mitigates this risk by clipping each client's contribution to a threshold $C$ and adding noise proportional to $C$.<n>We introduce DP-LAC, a method that first estimates an initial clipping threshold within an order of magnitude of the optimum using private histogram estimation.<n> Empirical results show that DP-LAC outperforms both state-of-the-art adaptive clipping methods and vanilla DP-SGD, achieving an average accuracy gain of $6.6%$
  arXiv  Detail & Related papers  (2026-05-11T09:32:21Z)
• On the Convergence of DP-SGD with Adaptive Clipping [56.24689348875711]
  Gradient Descent with gradient clipping is a powerful technique for enabling differentially private optimization.<n>This paper provides the first comprehensive convergence analysis of SGD with quantile clipping (QC-SGD)<n>We show how QC-SGD suffers from a bias problem similar to constant-threshold clipped SGD but can be mitigated through a carefully designed quantile and step size schedule.
  arXiv  Detail & Related papers  (2024-12-27T20:29:47Z)
• Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight [15.139854970044075]
  We introduce Differentially Private Per-sample Adaptive Scaling Clipping (DP-PSASC) This approach replaces traditional clipping with non-monotonous adaptive gradient scaling. Our theoretical and empirical analyses confirm that DP-PSASC preserves gradient privacy and delivers superior performance across diverse datasets.
  arXiv  Detail & Related papers  (2024-11-05T12:47:30Z)
• Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach [62.000948039914135]
  Using Differentially Private Gradient Descent with Gradient Clipping (DPSGD-GC) to ensure Differential Privacy (DP) comes at the cost of model performance degradation. We propose a new error-feedback (EF) DP algorithm as an alternative to DPSGD-GC. We establish an algorithm-specific DP analysis for our proposed algorithm, providing privacy guarantees based on R'enyi DP.
  arXiv  Detail & Related papers  (2023-11-24T17:56:44Z)
• Differentially Private Learning with Per-Sample Adaptive Clipping [8.401653565794353]
  We propose a Differentially Private Per-Sample Adaptive Clipping (DP-PSAC) algorithm based on a non-monotonic adaptive weight function. We show that DP-PSAC outperforms or matches the state-of-the-art methods on multiple main-stream vision and language tasks.
  arXiv  Detail & Related papers  (2022-12-01T07:26:49Z)
• Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization [94.06564567766475]
  DP-SGD and DP-NSGD mitigate the risk of large models memorizing sensitive training data. We show that these two algorithms achieve similar best accuracy while DP-NSGD is comparatively easier to tune than DP-SGD.
  arXiv  Detail & Related papers  (2022-06-27T03:45:02Z)
• Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger [39.93710312222771]
  Per-example clipping is a key algorithmic step that enables practical differential private (DP) training for deep learning models. We propose an easy-to-use replacement, called automatic clipping, that eliminates the need to tune R for any DPs.
  arXiv  Detail & Related papers  (2022-06-14T19:49:44Z)
• Layer Pruning on Demand with Intermediate CTC [50.509073206630994]
  We present a training and pruning method for ASR based on the connectionist temporal classification (CTC) We show that a Transformer-CTC model can be pruned in various depth on demand, improving real-time factor from 0.005 to 0.002 on GPU.
  arXiv  Detail & Related papers  (2021-06-17T02:40:18Z)
• MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients [112.00379151834242]
  We propose adaptive learning rate principle, in which the running mean of squared gradient in Adam is replaced by a weighted mean, with weights chosen to maximize the estimated variance each coordinate. This results in faster adaptation, which leads more desirable empirical convergence behaviors.
  arXiv  Detail & Related papers  (2020-06-21T21:47:43Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.