# When Gradient Clipping Becomes a Control Mechanism for Differential Privacy in Deep Learning

arXiv: https://arxiv.org/abs/2602.10584 · HTML: https://mlsanity.com/paper/2602.10584/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 24 of 24)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences](https://mlsanity.com/paper/1807.01647/) (2018)
- [Differentially Private Learning with Adaptive Clipping](https://mlsanity.com/paper/1905.03871/) (2019)
- [Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning](https://mlsanity.com/paper/1810.01075/) (2018)
- [Numerical Composition of Differential Privacy](https://mlsanity.com/paper/2106.02848/) (2021)
- [Understanding Gradient Clipping in Private SGD: A Geometric Perspective](https://mlsanity.com/paper/2006.15429/) (2020)
- [Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data](https://mlsanity.com/paper/2002.06716/) (2020)
- [Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger](https://mlsanity.com/paper/2206.07136/) (2022)
- [AdaCliP: Adaptive Clipping for Private SGD](https://mlsanity.com/paper/1908.07643/) (2019)
- [Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping](https://mlsanity.com/paper/2009.03106/) (2020)
- [Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks](https://mlsanity.com/paper/1901.08278/) (2019)
- [On the Convergence and Calibration of Deep Learning with Differential Privacy](https://mlsanity.com/paper/2106.07830/) (2021)
- [DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)](https://mlsanity.com/paper/2312.14334/) (2023)
- [Differentially Private Learning with Per-Sample Adaptive Clipping](https://mlsanity.com/paper/2212.00328/) (2022)
- [Differentially Private Adaptive Optimization with Delayed Preconditioners](https://mlsanity.com/paper/2212.00309/) (2022)
- [Faster Privacy Accounting via Evolving Discretization](https://mlsanity.com/paper/2207.04381/) (2022)
- [Differentially Private Sharpness-Aware Training](https://mlsanity.com/paper/2306.05651/) (2023)
- [DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling](https://mlsanity.com/paper/2210.09634/) (2022)
- [Differentially private fine-tuned NF-Net to predict GI cancer type](https://mlsanity.com/paper/2502.11329/) (2025)
- [DP-Adam-AC: Privacy-preserving Fine-Tuning of Localizable Language Models Using Adam Optimization with Adaptive Clipping](https://mlsanity.com/paper/2510.05288/) (2025)
- [Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping](https://mlsanity.com/paper/2506.01396/) (2025)
- [AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks](https://mlsanity.com/paper/2507.06525/) (2025)

## Built on by (most cited first, 1 of 1)

- [SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning](https://mlsanity.com/paper/2605.20450/) (2026)

## Abstract (arXiv)

Privacy-preserving training on sensitive data commonly relies on differentially private stochastic optimization with gradient clipping and Gaussian noise. The clipping threshold is a critical control knob: if set too small, systematic over-clipping induces optimization bias; if too large, injected noise dominates updates and degrades accuracy. Existing adaptive clipping methods often depend on per-example gradient norm statistics, adding computational overhead and introducing sensitivity to datasets and architectures. We propose a control-driven clipping strategy that adapts the threshold using a lightweight, weight-only spectral diagnostic computed from model parameters. At periodic probe steps, the method analyzes a designated weight matrix via spectral decomposition and estimates a heavy-tailed spectral indicator associated with training stability. This indicator is smoothed over time and fed into a bounded feedback controller that updates the clipping threshold multiplicatively in the log domain. Because the controller uses only parameters produced during privacy-preserving training, the resulting threshold updates are post-processing and do not increase privacy loss beyond that of the underlying DP optimizer under standard composition accounting.