Peregrine: One-Shot Fine-Tuning for FHE Inference of General Deep CNNs

TL;DR

This paper introduces Peregrine, a novel approach for converting general deep CNNs into FHE-compatible models using single-stage fine-tuning and a generalized packing scheme, enabling efficient privacy-preserving inference with minimal accuracy loss.

Contribution

The paper presents a new single-stage fine-tuning method and a generalized interleaved packing scheme that together facilitate end-to-end FHE inference for diverse CNN architectures.

Findings

Achieves competitive accuracy on CIFAR-10, ImageNet, and MS COCO.

First demonstration of FHE inference for YOLO object detection.

Enables high-resolution image processing under FHE constraints.

Abstract

We address two fundamental challenges in adapting general deep CNNs for FHE-based inference: approximating non-linear activations such as ReLU with low-degree polynomials while minimizing accuracy degradation, and overcoming the ciphertext capacity barrier that constrains high-resolution image processing on FHE inference. Our contributions are twofold: (1) a single-stage fine-tuning (SFT) strategy that directly converts pre-trained CNNs into FHE-friendly forms using low-degree polynomials, achieving competitive accuracy with minimal training overhead; and (2) a generalized interleaved packing (GIP) scheme that is compatible with feature maps of virtually arbitrary spatial resolutions, accompanied by a suite of carefully designed homomorphic operators that preserve the GIP-form encryption throughout computation. These advances enable efficient, end-to-end FHE inference across diverse CNN architectures. Experiments on CIFAR-10, ImageNet, and MS COCO demonstrate that the FHE-friendly CNNs obtained via our SFT strategy achieve accuracy comparable to baselines using ReLU or SiLU activations. Moreover, this work presents the first demonstration of FHE-based inference for YOLO architectures in object detection leveraging low-degree polynomial activations.