Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption

• URL: http://arxiv.org/abs/2304.14836v2
• Date: Sun, 11 Jun 2023 10:07:52 GMT
• Title: Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption
• Authors: Moran Baruch, Nir Drucker, Gilad Ezov, Yoav Goldberg, Eyal Kushnir, Jenny Lerner, Omri Soceanu and Itamar Zimerman
• Abstract summary: Training large-scale CNNs that during inference can be run under Homomorphic Encryption (HE) is challenging. We provide a novel training method for large CNNs such as ResNet-152 and ConvNeXt models.
• Score: 33.35896071292604
• License: http://creativecommons.org/licenses/by/4.0/
• 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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