Towards hyperparameter-free optimization with differential privacy

• URL: http://arxiv.org/abs/2503.00703v1
• Date: Sun, 02 Mar 2025 02:59:52 GMT
• Title: Towards hyperparameter-free optimization with differential privacy
• Authors: Zhiqi Bu, Ruixuan Liu,
• Abstract summary: Differential privacy (DP) is a privacy-preserving paradigm that protects the training data when training deep learning models.<n>In this work, we adapt the automatic learning rate schedule to DP optimization for any models and achieves state-of-the-art DP performance on various language and vision tasks.
• Score: 9.193537596304669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differential privacy (DP) is a privacy-preserving paradigm that protects the training data when training deep learning models. Critically, the performance of models is determined by the training hyperparameters, especially those of the learning rate schedule, thus requiring fine-grained hyperparameter tuning on the data. In practice, it is common to tune the learning rate hyperparameters through the grid search that (1) is computationally expensive as multiple runs are needed, and (2) increases the risk of data leakage as the selection of hyperparameters is data-dependent. In this work, we adapt the automatic learning rate schedule to DP optimization for any models and optimizers, so as to significantly mitigate or even eliminate the cost of hyperparameter tuning when applied together with automatic per-sample gradient clipping. Our hyperparameter-free DP optimization is almost as computationally efficient as the standard non-DP optimization, and achieves state-of-the-art DP performance on various language and vision tasks.

Related papers

• DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction [57.83978915843095]
  This paper introduces DiSK, a novel framework designed to significantly enhance the performance of differentially private gradients. To ensure practicality for large-scale training, we simplify the Kalman filtering process, minimizing its memory and computational demands.
  arXiv  Detail & Related papers  (2024-10-04T19:30:39Z)
• Adaptive Preference Scaling for Reinforcement Learning with Human Feedback [103.36048042664768]
  Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values. We propose a novel adaptive preference loss, underpinned by distributionally robust optimization (DRO) Our method is versatile and can be readily adapted to various preference optimization frameworks.
  arXiv  Detail & Related papers  (2024-06-04T20:33:22Z)
• AdaLomo: Low-memory Optimization with Adaptive Learning Rate [59.64965955386855]
  We introduce low-memory optimization with adaptive learning rate (AdaLomo) for large language models. AdaLomo results on par with AdamW, while significantly reducing memory requirements, thereby lowering the hardware barrier to training large language models.
  arXiv  Detail & Related papers  (2023-10-16T09:04:28Z)
• DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework [31.628466186344582]
  We introduce DP-HyPO, a pioneering framework for adaptive'' private hyperparameter optimization. We provide a comprehensive differential privacy analysis of our framework. We empirically demonstrate the effectiveness of DP-HyPO on a diverse set of real-world datasets.
  arXiv  Detail & Related papers  (2023-06-09T07:55:46Z)
• Hyperparameter Optimization through Neural Network Partitioning [11.6941692990626]
  We propose a simple and efficient way for optimizing hyper parameters in neural networks. Our method partitions the training data and a neural network model into $K$ data shards and parameter partitions. We demonstrate that we can apply this objective to optimize a variety of different hyper parameters in a single training run.
  arXiv  Detail & Related papers  (2023-04-28T11:24:41Z)
• A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization [57.450449884166346]
  We propose an adaptive HPO method to account for the privacy cost of HPO. We obtain state-of-the-art performance on 22 benchmark tasks, across computer vision and natural language processing, across pretraining and finetuning.
  arXiv  Detail & Related papers  (2022-12-08T18:56:37Z)
• AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning [72.54359545547904]
  We propose a gradient-based subset selection framework for hyper- parameter tuning. We show that using gradient-based data subsets for hyper- parameter tuning achieves significantly faster turnaround times and speedups of 3$times$-30$times$.
  arXiv  Detail & Related papers  (2022-03-15T19:25:01Z)
• Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning [0.0]
  In reinforcement learning (RL), the information content of data gathered by the learning agent is dependent on the setting of many hyper- parameters. In this work, a novel approach for autonomous hyper- parameter setting using Bayesian optimization is proposed. Experiments reveal promising results compared to other manual tweaking and optimization-based approaches.
  arXiv  Detail & Related papers  (2021-12-15T13:10:44Z)
• The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection [12.38071940409141]
  We show that standard composition tools outperform more advanced techniques in many settings. We draw upon limiting behaviour of Adam in the DP setting to design a new and more efficient tool.
  arXiv  Detail & Related papers  (2021-11-09T01:56:56Z)
• Online hyperparameter optimization by real-time recurrent learning [57.01871583756586]
  Our framework takes advantage of the analogy between hyperparameter optimization and parameter learning in neural networks (RNNs) It adapts a well-studied family of online learning algorithms for RNNs to tune hyperparameters and network parameters simultaneously. This procedure yields systematically better generalization performance compared to standard methods, at a fraction of wallclock time.
  arXiv  Detail & Related papers  (2021-02-15T19:36:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.