Can I read GINN: Fast GPU-TEE Based Integrity for Neural Network Training on EtoBox?

GINN: Fast GPU-TEE Based Integrity for Neural Network Training by Aref Asvadishirehjini; Murat Kantarcioglu; Bradley Malin is a scholarly article available to read on EtoBox.

What is GINN: Fast GPU-TEE Based Integrity for Neural Network Training about?

Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a wide variety of applications, ranging from self-driving cars to COVID-19 diagnosis. To support the computational power necessary to train a DNN, cloud environments with dedicated Graphical Processing Unit (GPU) hardware support have emerged as critical infrastructure. However, there are many integrity challenges associated with outsourcing the computation to use GPU power, due to its inherent lack of safeguards to ensure computational integrity. Various approaches have been developed to address these challenges, building on trusted execution environments (TEE). Yet, no existing approach scales up to support realistic integrity-preserving DNN model training for heavy workloads (e.g., deep architectures and millions of training examples) without sustaining a significant performance hit. To mitigate the running time difference between pure TEE (i.e., full integrity) and pure GPU (i.e., no integrity) , we combine random verification of selected computation steps with systematic adjustments of DNN hyperparameters (e.g., a narrow gradient clipping range), which limits the attacker's ability to shi

Author

Aref Asvadishirehjini; Murat Kantarcioglu; Bradley Malin

Publisher

ACM

Published

2022

Language

EN