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arXiv · 2304.14836

Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption

Moran Baruch·Nir Drucker·Gilad Ezov·Yoav Goldberg·Eyal Kushnir·Jenny Lerner·Omri Soceanu·Itamar Zimerman
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Abstract

Training large-scale CNNs that during inference can be run under Homomorphic Encryption (HE) is challenging due to the need to use only polynomial operations. This limits HE-based solutions adoption. We address this challenge and pioneer in providing a novel training method for large polynomial CNNs such as ResNet-152 and ConvNeXt models, and achieve promising accuracy on encrypted samples on large-scale dataset such as ImageNet. Additionally, we provide optimization insights regarding activation functions and skip-connection latency impacts, enhancing HE-based evaluation efficiency. Finally, to demonstrate the robustness of our method, we provide a polynomial adaptation of the CLIP model for secure zero-shot prediction, unlocking unprecedented capabilities at the intersection of HE and transfer learning.

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Moran Baruch, Nir Drucker, Gilad Ezov, Yoav Goldberg, Eyal Kushnir, Jenny Lerner, Omri Soceanu, Itamar Zimerman. 2023-04-26. Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption. https://arxiv.org/abs/2304.14836

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