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arXiv · 2504.01933

Hessian-aware Training for Enhancing DNNs Resilience to Parameter Corruptions

Tahmid Hasan Prato·Seijoon Kim·Lizhong Chen·Sanghyun Hong
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Abstract

Deep neural networks are not resilient to parameter corruptions: even a single-bitwise error in their parameters in memory can cause an accuracy drop of over 10%, and in the worst cases, up to 99%. This susceptibility poses great challenges in deploying models on computing platforms, where adversaries can induce bit-flips through software or bitwise corruptions may occur naturally. Most prior work addresses this issue with hardware or system-level approaches, such as integrating additional hardware components to verify a model's integrity at inference. However, these methods have not been widely deployed as they require infrastructure or platform-wide modifications. In this paper, we propose a new approach to addressing this issue: training models to be more resilient to bitwise corruptions to their parameters. Our approach, Hessian-aware training, promotes models with $flatter$ loss surfaces. We show that, while there have been training methods, designed to improve generalization through Hessian-based approaches, they do not enhance resilience to parameter corruptions. In contrast, models trained with our method demonstrate increased resilience to parameter corruptions, particularly with a 20$-$50% reduction in the number of bits whose individual flipping leads to a 90$-$100% accuracy drop. Moreover, we show the synergy between ours and existing hardware and system-level defenses.

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Tahmid Hasan Prato, Seijoon Kim, Lizhong Chen, Sanghyun Hong. 2025-04-02. Hessian-aware Training for Enhancing DNNs Resilience to Parameter Corruptions. https://arxiv.org/abs/2504.01933

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