Privacy-Preserving Optimizers

Privacy-preserving optimizers train models under differential privacy, typically by clipping per-sample gradients and adding calibrated noise to updates. This page lists differentially private optimization methods and reference libraries, from the original DP-SGD to later variants that reduce clipping bias, correct moment estimates, or filter privacy noise.

Optimizer	Venue	Paper	Code
DP-SGD	CCS 2016	Deep Learning with Differential Privacy	official
DP-LSSGD	MSML 2020	DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM	official
DP-PASGD	arXiv 2020	Differentially Private Federated Learning for Resource-Constrained Internet of Things	—
DP-SGD-JL	NeurIPS 2021	Fast and Memory Efficient Differentially Private-SGD via JL Projections	—
Opacus	arXiv 2021	Opacus: User-Friendly Differential Privacy Library in PyTorch	official
A(DP)²SGD	TPAMI 2022	A(DP)²SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy	—
DPIS	CCS 2022	DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling	—
Top-DP	TCSVT 2022	Topology-aware Differential Privacy for Decentralized Image Classification	—
ANSGD	arXiv 2023	Learning across Data Owners with Joint Differential Privacy	—
DP-FedSAM	CVPR 2023	Make Landscape Flatter in Differentially Private Federated Learning	official
AClipped-dpSGD	Machine Learning 2024	Efficient Private SCO for Heavy-Tailed Data via Averaged Clipping	—
DiceSGD	ICLR 2024	Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach	official
DOPPLER	NeurIPS 2024	DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction	—
DP-AdamBC	AAAI 2024	DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)	official
FedLAP-DP	PoPETs 2024	FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations	official
DC-SGD	TIFS 2025	DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation	—
DP-AdamW	ICML Workshop 2025	DP-AdamW: Investigating Decoupled Weight Decay and Bias Correction in Private Deep Learning	—
DP-MicroAdam	arXiv 2025	DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning	—
DPZV	arXiv 2025	Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization	—
GeoDP	ICDE 2025	Analyzing and Optimizing Perturbation of DP-SGD Geometrically	official
Interleaved-ShuffleG	arXiv 2025	Improving the Convergence of Private Shuffled Gradient Methods with Public Data	—
Logit-DP	ICLR 2025	Differentially Private Optimization for Non-Decomposable Objective Functions	—
SPARTA	KDD 2025	SPARTA: An Optimization Framework for Differentially Private Sparse Fine-Tuning	official
DP-λCGD	arXiv 2026	DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training	—
PINA	ICASSP 2026	Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation	—
RaCO-DP	ICLR 2026	Private Rate-Constrained Optimization with Applications to Fair Learning	official
DP-MacAdam	arXiv 2026	DP-MacAdam: Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum	—
FO-DP-SGD	arXiv 2026	Deep Learning under Fractional-Order Differential Privacy	—
Hyperparameter-free DP optimization (GeN-DP)	ICLR 2025	Towards hyperparameter-free optimization with differential privacy	—
DP-Muon	arXiv 2026	DP-Muon: Differentially Private Optimization via Matrix-Orthogonalized Momentum	—
TP-TopK	arXiv 2026	When Do Fewer Coordinates Suffice in DP-SGD?	—
DPDL	arXiv 2026	DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data	—
DP-SGD-RC	ICML 2026	Efficient DP-SGD for LLMs with Randomized Clipping	—
PRISM	ICML 2026	PRISM: Gauge-Invariant Tangent-Space Differentially Private LoRA	—
SMA-DP-SGD	arXiv 2026	SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning	—
FiBeR	arXiv 2026	FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction	—
DP-KFC	ICML 2026	DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning	official
DP-FedAdamW	CVPR 2026	DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models	—
Lap2	IEEE CSF 2026	Lap2: Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory	official
Clip21-SGD2M	arXiv 2025	Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy	—