FlashDP: Private Training Large Language Models with Efficient DP-SGD
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Liangyu, Wang, Junxiao, Ren, Jie, Xiang, Zihang, Keyes, David E., Wang, Di |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
How Private are DP-SGD Implementations?
by: Chua, Lynn, et al.
Published: (2024)
by: Chua, Lynn, et al.
Published: (2024)
DP-SGD with weight clipping
by: Barczewski, Antoine, et al.
Published: (2023)
by: Barczewski, Antoine, et al.
Published: (2023)
Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?
by: Dong, Andy, et al.
Published: (2026)
by: Dong, Andy, et al.
Published: (2026)
DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
by: Sha, Haichao, et al.
Published: (2026)
by: Sha, Haichao, et al.
Published: (2026)
To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms
by: Ganesh, Arun
Published: (2024)
by: Ganesh, Arun
Published: (2024)
R+R:Understanding Hyperparameter Effects in DP-SGD
by: Morsbach, Felix, et al.
Published: (2024)
by: Morsbach, Felix, et al.
Published: (2024)
DP-SGD Without Clipping: The Lipschitz Neural Network Way
by: Bethune, Louis, et al.
Published: (2023)
by: Bethune, Louis, et al.
Published: (2023)
On the Convergence of DP-SGD with Adaptive Clipping
by: Shulgin, Egor, et al.
Published: (2024)
by: Shulgin, Egor, et al.
Published: (2024)
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
by: Cebere, Tudor, et al.
Published: (2024)
by: Cebere, Tudor, et al.
Published: (2024)
Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
by: Ertan, Murat Bilgehan, et al.
Published: (2026)
by: Ertan, Murat Bilgehan, et al.
Published: (2026)
Balls-and-Bins Sampling for DP-SGD
by: Chua, Lynn, et al.
Published: (2024)
by: Chua, Lynn, et al.
Published: (2024)
DP-LDMs: Differentially Private Latent Diffusion Models
by: Liu, Michael F., et al.
Published: (2023)
by: Liu, Michael F., et al.
Published: (2023)
Lap2: Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory
by: Mohammady, Meisam, et al.
Published: (2026)
by: Mohammady, Meisam, et al.
Published: (2026)
Closed-Form Bounds for DP-SGD against Record-level Inference
by: Cherubin, Giovanni, et al.
Published: (2024)
by: Cherubin, Giovanni, et al.
Published: (2024)
LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models
by: Yang, Qin, et al.
Published: (2024)
by: Yang, Qin, et al.
Published: (2024)
DP-TLDM: Differentially Private Tabular Latent Diffusion Model
by: Zhu, Chaoyi, et al.
Published: (2024)
by: Zhu, Chaoyi, et al.
Published: (2024)
Training Set Reconstruction from Differentially Private Forests: How Effective is DP?
by: Gorgé, Alice, et al.
Published: (2025)
by: Gorgé, Alice, et al.
Published: (2025)
DP-KAN: Differentially Private Kolmogorov-Arnold Networks
by: Kalinin, Nikita P., et al.
Published: (2024)
by: Kalinin, Nikita P., et al.
Published: (2024)
Revisiting Differentially Private Hyper-parameter Tuning
by: Xiang, Zihang, et al.
Published: (2024)
by: Xiang, Zihang, et al.
Published: (2024)
Have it your way: Individualized Privacy Assignment for DP-SGD
by: Boenisch, Franziska, et al.
Published: (2023)
by: Boenisch, Franziska, et al.
Published: (2023)
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
by: Thudi, Anvith, et al.
Published: (2023)
by: Thudi, Anvith, et al.
Published: (2023)
It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With Non-Convex Loss
by: Annamalai, Meenatchi Sundaram Muthu Selva
Published: (2024)
by: Annamalai, Meenatchi Sundaram Muthu Selva
Published: (2024)
Scalable DP-SGD: Shuffling vs. Poisson Subsampling
by: Chua, Lynn, et al.
Published: (2024)
by: Chua, Lynn, et al.
Published: (2024)
Too Good to be True? Turn Any Model Differentially Private With DP-Weights
by: Zagardo, David
Published: (2024)
by: Zagardo, David
Published: (2024)
Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds
by: van Dijk, Marten, et al.
Published: (2026)
by: van Dijk, Marten, et al.
Published: (2026)
Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning
by: Fu, Xiaowen, et al.
Published: (2024)
by: Fu, Xiaowen, et al.
Published: (2024)
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
by: Lim, Juntaek, et al.
Published: (2024)
by: Lim, Juntaek, et al.
Published: (2024)
Rethinking the Security of DP-SGD: A Corrected Analysis of Differentially Private Machine Learning
by: Wang, Wenhao, et al.
Published: (2026)
by: Wang, Wenhao, et al.
Published: (2026)
DP-SPRT: Differentially Private Sequential Probability Ratio Tests
by: Michel, Thomas, et al.
Published: (2025)
by: Michel, Thomas, et al.
Published: (2025)
The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2025)
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2025)
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
by: Tian, Xizhi, et al.
Published: (2025)
by: Tian, Xizhi, et al.
Published: (2025)
Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
by: Ngo, Hoang M., et al.
Published: (2026)
by: Ngo, Hoang M., et al.
Published: (2026)
DP-TabICL: In-Context Learning with Differentially Private Tabular Data
by: Carey, Alycia N., et al.
Published: (2024)
by: Carey, Alycia N., et al.
Published: (2024)
DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization
by: Wang, Lionel Z., et al.
Published: (2026)
by: Wang, Lionel Z., et al.
Published: (2026)
Preserving Node-level Privacy in Graph Neural Networks
by: Xiang, Zihang, et al.
Published: (2023)
by: Xiang, Zihang, et al.
Published: (2023)
SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
by: Nahid, Md Mahadi Hasan, et al.
Published: (2024)
by: Nahid, Md Mahadi Hasan, 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)
by: Mehmood, Haaris, et al.
Published: (2026)
DP-SNP-TIHMM: Differentially Private, Time-Inhomogeneous Hidden Markov Models for Synthesizing Genome-Wide Association Datasets
by: Rahimian, Shadi, et al.
Published: (2025)
by: Rahimian, Shadi, et al.
Published: (2025)
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
by: Liu, Yanming, et al.
Published: (2024)
by: Liu, Yanming, et al.
Published: (2024)
Similar Items
-
How Private are DP-SGD Implementations?
by: Chua, Lynn, et al.
Published: (2024) -
DP-SGD with weight clipping
by: Barczewski, Antoine, et al.
Published: (2023) -
Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?
by: Dong, Andy, et al.
Published: (2026) -
DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
by: Sha, Haichao, et al.
Published: (2026) -
To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)