Accelerating Differentially Private Fine-Tuning with Momentum and Optimal Hyperparameter Scaling

Core Concepts

By carefully integrating techniques from prior work, including momentum acceleration and a new linear scaling rule for hyperparameters, we obtain new state-of-the-art performance on benchmark computer vision and natural language processing tasks under differential privacy constraints.

Abstract

The paper presents DP-RAFT, a recipe for differentially private fine-tuning that can recover the same performance as non-private fine-tuning. The key insights are: A new linear scaling rule for hyperparameters - as the privacy budget ε increases, the optimal total step size (learning rate × number of iterations) increases linearly. This mitigates the impact of noise on the optimization trajectory. Use of momentum to accelerate convergence - the exponentially moving average of noisy gradients has higher signal-to-noise ratio than individual gradients. Full batch gradient computation - this optimizes the signal-to-noise ratio of the update and enables using large step sizes. Zero initialization of the classifier weights - this mitigates the variance in DP-GD. Unit norm clipping of per-sample gradients - this keeps step sizes large without introducing significant bias. The authors evaluate DP-RAFT on four computer vision tasks (CIFAR10, CIFAR100, STL10, FashionMNIST) and one natural language processing task (PersonaChat), reporting new state-of-the-art results for ε ∈ [0.01, 1.0]. They notably recover the same performance as non-private fine-tuning for CIFAR10 (99%) for ε = 1, δ = 1e-5.

Stats

"We notably recover the same performance as non-private fine-tuning for CIFAR10 (99%) for ε = 1, δ = 1e-5." "We obtain new state-of-the-art performance on CIFAR10, CIFAR100, FashionMNIST, STL10, and PersonaChat."

Quotes

"Our key insight is that this new linear scaling rule turns existing intuition on noisy optimization into an actionable strategy for hyperparameter selection." "Momentum is shown to provably benefit normalized SGD (Cutkosky and Mehta, 2020). In Fig. 4 we observe that momentum complements our new linear scaling rule and accelerates convergence." "We use DP-GD instead of DP-SGD in all other experiments, removing the batch size from the hyperparameter tuning process and improving the overall privacy cost of deploying our baselines (Papernot and Steinke, 2021)."

Key Insights Distilled From

A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization

by Ashwinee Pan... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2212.04486.pdf

Deeper Inquiries

How can the proposed techniques be extended to other machine learning tasks beyond computer vision and natural language processing

The techniques proposed in the paper can be extended to other machine learning tasks beyond computer vision and natural language processing by adapting them to the specific requirements and characteristics of different tasks. For tasks such as speech recognition, reinforcement learning, or time series analysis, the key insights from the paper can still be applied. For example, in speech recognition, the linear scaling rule can be used to optimize hyperparameters for training models on audio data. Similarly, in reinforcement learning, the momentum acceleration technique can be employed to improve convergence speed and performance. By customizing the techniques to suit the data and requirements of different tasks, the benefits of differential privacy and accelerated fine-tuning can be realized across a wide range of machine learning applications.

What are the potential limitations or drawbacks of the linear scaling rule for hyperparameters, and under what conditions might it not be optimal

While the linear scaling rule for hyperparameters offers significant benefits in terms of optimizing the privacy-utility tradeoff in differentially private fine-tuning, there are potential limitations and drawbacks to consider. One limitation is that the linear scaling rule may not be optimal for all datasets and models. In cases where the dataset distribution or model architecture deviates significantly from the assumptions underlying the linear scaling rule, the performance may not be as optimal. Additionally, the linear scaling rule may not be suitable for tasks where the optimal hyperparameters do not scale linearly with the privacy budget. In such cases, a more adaptive or dynamic hyperparameter tuning strategy may be more effective. Furthermore, the linear scaling rule may not address all aspects of the privacy-utility tradeoff, such as the impact of model complexity or the choice of optimization algorithms. It is important to carefully evaluate the applicability of the linear scaling rule in different scenarios and consider alternative strategies when necessary.

Can the insights from this work be applied to improve the privacy-utility tradeoff in other differentially private machine learning algorithms beyond fine-tuning

The insights from this work can be applied to improve the privacy-utility tradeoff in other differentially private machine learning algorithms beyond fine-tuning by focusing on key aspects such as noise reduction, optimization trajectory, and hyperparameter selection. For example, in differentially private federated learning, the momentum acceleration technique can be used to improve convergence speed and model performance. Similarly, the concept of optimizing the signal-to-noise ratio can be applied to differentially private clustering algorithms to enhance the quality of cluster assignments while preserving privacy. By incorporating the principles of accelerated training with differential privacy and careful hyperparameter selection, various machine learning algorithms can achieve better performance while maintaining strong privacy guarantees. It is essential to adapt and tailor these insights to the specific requirements and constraints of each algorithm to effectively balance privacy and utility.