Cover Image

DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wei, Chengkun, Li, Weixian, Gong, Chen, Chen, Wenzhi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization
by: Feng, Ce, et al.
Published: (2024)

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

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

PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance
by: Sha, Haichao, et al.
Published: (2023)

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

DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling
by: Wei, Jianxin, et al.
Published: (2022)

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

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

DP-SGD Without Clipping: The Lipschitz Neural Network Way
by: Bethune, Louis, et al.
Published: (2023)

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

Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping
by: Şimşekli, Umut, et al.
Published: (2024)

Differentially Private Block-wise Gradient Shuffle for Deep Learning
by: Zagardo, David
Published: (2024)

An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses
by: Liang, Hao, et al.
Published: (2025)

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
by: Steinke, Thomas, et al.
Published: (2024)

Differentially Private Model Merging
by: Yin, Qichuan, et al.
Published: (2026)

DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately
by: Wu, Huiwen, et al.
Published: (2024)

Improved Algorithms for Differentially Private Language Model Alignment
by: Chen, Keyu, et al.
Published: (2025)

Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
by: Kalinin, Nikita P., et al.
Published: (2025)

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

Differentially Private Distributed Inference
by: Papachristou, Marios, et al.
Published: (2024)

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
by: Piran, Fardin Jalil, et al.
Published: (2024)

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

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

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
by: Li, Yuecheng, et al.
Published: (2024)

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

Differentially Private Worst-group Risk Minimization
by: Zhou, Xinyu, et al.
Published: (2024)

Differentially Private Iterative Screening Rules for Linear Regression
by: Khanna, Amol, et al.
Published: (2025)

Differentially Private In-Context Learning with Nearest Neighbor Search
by: Koskela, Antti, et al.
Published: (2025)

Evaluating Differentially Private Generation of Domain-Specific Text
by: Sun, Yidan, et al.
Published: (2025)

Differentially Private Deep Model-Based Reinforcement Learning
by: Rio, Alexandre, et al.
Published: (2024)

Provable Differentially Private Computation of the Cross-Attention Mechanism
by: Ke, Yekun, et al.
Published: (2024)

DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
by: Mehmood, Haaris, et al.
Published: (2026)

Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification
by: Zhou, Duo, et al.
Published: (2025)

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
by: Pelikan, Martin, et al.
Published: (2023)

Differentially Private Synthetic Data Release for Topics API Outputs
by: Dick, Travis, et al.
Published: (2025)

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
by: Gao, Fengyu, et al.
Published: (2024)

SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding
by: Park, Chanho, et al.
Published: (2024)

Differentially Private Synthetic Data Generation Using Context-Aware GANs
by: Kotal, Anantaa, et al.
Published: (2025)

Differentially Private Preference Data Synthesis for Large Language Model Alignment
by: Gao, Fengyu, et al.
Published: (2026)

Reconstruction of Differentially Private Text Sanitization via Large Language Models
by: Pang, Shuchao, et al.
Published: (2024)