{"total":36,"rows":[{"id":"2406.02958","title":"PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs","created":"2024-06-05","cited_by_count":29,"area":null},{"id":"2405.19524","title":"AI Risk Management Should Incorporate Both Safety and Security","created":"2024-05-29","cited_by_count":17,"area":null},{"id":"2411.04205","title":"Scalable DP-SGD: Shuffling vs. Poisson Subsampling","created":"2024-11-06","cited_by_count":16,"area":null},{"id":"2402.07818","title":"Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning","created":"2024-02-12","cited_by_count":14,"area":null},{"id":"2412.16802","title":"Balls-and-Bins Sampling for DP-SGD","created":"2024-12-21","cited_by_count":14,"area":null},{"id":"2410.12085","title":"Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning","created":"2024-10-15","cited_by_count":10,"area":null},{"id":"2502.15436","title":"Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning","created":"2025-02-21","cited_by_count":10,"area":null},{"id":"2503.00703","title":"Towards hyperparameter-free optimization with differential privacy","created":"2025-03-02","cited_by_count":7,"area":null},{"id":"2403.04124","title":"Privacy-preserving Fine-tuning of Large Language Models through Flatness","created":"2024-03-07","cited_by_count":6,"area":null},{"id":"2501.04287","title":"ElasticZO: A Memory-Efficient On-Device Learning with Combined Zeroth- and First-Order Optimization","created":"2025-01-08","cited_by_count":5,"area":null},{"id":"2502.03604","title":"Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Training","created":"2025-02-05","cited_by_count":5,"area":null},{"id":"2505.18886","title":"KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning","created":"2025-05-24","cited_by_count":5,"area":null},{"id":"2506.16666","title":"The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing","created":"2025-06-20","cited_by_count":5,"area":null},{"id":"2406.14362","title":"Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization","created":"2024-06-20","cited_by_count":4,"area":null},{"id":"2601.10237","title":"Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD","created":"2026-01-15","cited_by_count":4,"area":null},{"id":"2511.18006","title":"Understanding Private Learning From Feature Perspective","created":"2025-11-22","cited_by_count":3,"area":null},{"id":"2502.13193","title":"Private Text Generation by Seeding Large Language Model Prompts","created":"2025-02-18","cited_by_count":2,"area":null},{"id":"2506.15588","title":"Memory-Efficient Differentially Private Training with Gradient Random Projection","created":"2025-06-18","cited_by_count":2,"area":null},{"id":"2510.18228","title":"Towards Fast LLM Fine-tuning through Zeroth-Order Optimization with Projected Gradient-Aligned Perturbations","created":"2025-10-21","cited_by_count":2,"area":null},{"id":"2510.19953","title":"On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization","created":"2025-10-22","cited_by_count":2,"area":null},{"id":"2511.10859","title":"Private Zeroth-Order Optimization with Public Data","created":"2025-11-13","cited_by_count":2,"area":null},{"id":"2502.20565","title":"Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization","created":"2025-02-27","cited_by_count":1,"area":null},{"id":"2505.13954","title":"VAMO: Efficient Zeroth-Order Variance Reduction for SGD with Faster Convergence","created":"2025-05-20","cited_by_count":1,"area":null},{"id":"2602.02962","title":"Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning","created":"2026-02-03","cited_by_count":1,"area":null},{"id":"2605.06505","title":"PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2605.15622","title":"Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2605.24879","title":"Efficient DP-SGD for LLMs with Randomized Clipping","created":"2026-05-24","cited_by_count":1,"area":null},{"id":"2504.00411","title":"Forward Learning with Differential Privacy","created":"2025-04-01","cited_by_count":0,"area":null},{"id":"2506.00158","title":"Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2507.05610","title":"On the Inherent Privacy of Zeroth Order Projected Gradient Descent","created":"2025-07-08","cited_by_count":0,"area":null},{"id":"2508.15523","title":"Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2510.16157","title":"Zeroth-Order Sharpness-Aware Learning with Exponential Tilting","created":"2025-10-17","cited_by_count":0,"area":null},{"id":"2603.07389","title":"Feed m Birds with One Scone: Accelerating Multi-task Gradient Balancing via Bi-level Optimization","created":"2026-03-08","cited_by_count":0,"area":null},{"id":"2603.09356","title":"Democratising Clinical AI through Dataset Condensation for Classical Clinical Models","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2604.09940","title":"New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework","created":"2026-04-10","cited_by_count":0,"area":null},{"id":"2608.03277","title":"Noise-Aware Shrinkage for Differentially Private Zeroth-Order Fine-Tuning of Large Language Models","created":"2026-08-04","cited_by_count":0,"area":null}],"next_cursor":null}