Decision-Aware Quadratic ReLU Replacement for HE-Friendly Inference
Fully homomorphic encryption (FHE) supports only additions and multiplications, so FHE-only neural-network inference typically replaces ReLU with polynomials fitted over empirical activation intervals. Such interval fitting often requires higher-degree polynomials to control activation error, incurring homomorphic evaluation costs, while classification is determined by the final logit decision. We revisit ReLU replacement from a decision-aware perspective: given a trained single-hidden-layer ReLU MLP and a specified calibration set, can an HE-friendly low-degree polynomial replace ReLU without retraining while preserving calibration-set decisions? We focus on quadratic replacement, the lo...
| Authors | Rui Li, Wenyuan Wu, Weijie Miao |
|---|---|
| Primary category | cs.CR |
| Published | 21 May 2026 |
| Last updated at source | 22 May 2026 |
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