Approximating Complex Activation Functions for Secure Computation with Compact

Core Concepts

Generating accurate piece-wise polynomial approximations of complex activation functions for secure computation.

Abstract

The article introduces Compact, a method to approximate complex activation functions for secure multi-party computation. It addresses the inefficiency of current techniques in handling non-linear activation functions in deep neural networks. By using piece-wise polynomial approximations, Compact achieves near-identical model accuracy without imposing restrictions on model training. The approach incorporates input density awareness and optimization techniques to generate efficient approximations. Extensive evaluations show that Compact is faster than existing methods for DNN models with many hidden layers while maintaining negligible accuracy loss.

Stats

State-of-the-art approaches are 2×—5× slower than Compact for DNN models with many hidden layers. Approximation error threshold is set at 𝜈 = 10^-2.

Quotes

"We present Compact, which produces piece-wise polynomial approximations of complex activation functions that can be used with state-of-the-art MPC techniques." "Our work accelerates easy adoption of MPC techniques to provide user data privacy even when the queried DNN models consist of a number of hidden layers and complex activation functions."

Key Insights Distilled From

Compact

by Mazharul Isl... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2309.04664.pdf

Deeper Inquiries

How does the incorporation of input density awareness improve the accuracy of the approximations

Incorporating input density awareness improves the accuracy of the approximations by focusing on regions where the probability distribution of inputs is higher. In the context of secure computation for deep neural networks, this means that the piece-wise polynomial approximations generated by Compact are designed to be more accurate in areas where inputs are more likely to fall. By considering how frequently certain values occur as inputs to complex activation functions, Compact can prioritize those regions for more precise approximation. This approach ensures that the approximation error is minimized in areas where it matters most, leading to improved overall accuracy.

What are the implications of setting a fixed approximation error threshold versus dynamically adjusting it as done in Compact

Setting a fixed approximation error threshold can be limiting because different DNN models or datasets may require varying levels of accuracy in their approximations. By dynamically adjusting the threshold as done in Compact, practitioners have more flexibility and can find an optimal balance between inference accuracy loss and performance overhead. This adaptive approach allows Compact to systematically search for an appropriate threshold that minimizes inference accuracy loss while ensuring reduced performance overhead. It also relieves practitioners from manually determining an appropriate threshold for each scenario, making the process more efficient and effective.

How can the concept of weighted mean approximation error be applied to other areas within machine learning beyond secure computation

The concept of weighted mean approximation error used in Compact can be applied to other areas within machine learning beyond secure computation. For example: Model Training: Weighted mean approximation error could be utilized during model training to evaluate how well a particular model fits a given dataset based on the distribution of data points. Anomaly Detection: In anomaly detection tasks, weighted mean approximation error could help identify unusual patterns or outliers by considering both prediction errors and data point probabilities. Reinforcement Learning: When optimizing policies in reinforcement learning algorithms, incorporating weighted mean approximation error could guide policy updates based on state-action pairs' importance. By leveraging P(𝑥) information through EMean calculation similar techniques like dynamic adjustment thresholds could enhance various machine learning processes' efficiency and effectiveness across different domains beyond secure computation scenarios.