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Training large-scale CNNs that during inference can be run under HomomorphicEncryption (HE) is challenging due to the need to use only polynomialoperations. This limits HE-based solutions adoption. We address this challengeand pioneer in providing a novel training method for large polynomial CNNs suchas ResNet-152 and ConvNeXt models, and achieve promising accuracy on encryptedsamples on large-scale dataset such as ImageNet. Additionally, we provideoptimization insights regarding activation functions and skip-connectionlatency impacts, enhancing HE-based evaluation efficiency. Finally, todemonstrate the robustness of our method, we provide a polynomial adaptation ofthe CLIP model for secure zero-shot prediction, unlocking unprecedentedcapabilities at the intersection of HE and transfer learning.

Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption