arXiv:2605.22237v1 Announce Type: cross Abstract: 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-f
Ongoing research into Fully Homomorphic Encryption (FHE) is pushing towards practical, efficient AI inference methods without compromising data privacy.
This research addresses a critical technical barrier for integrating AI models with FHE, which is essential for privacy-preserving AI applications in sensitive domains.
New methods for ReLU replacement in FHE-friendly inference could significantly reduce computational overhead, making secure AI more viable for real-world deployment.
- · Privacy-focused AI developers
- · Healthcare sector
- · Financial institutions
- · Cloud computing providers
- · Companies relying solely on unencrypted data for AI
- · Legacy AI inference hardware
More widespread adoption of privacy-preserving AI services across various industries.
Increased demand for specialized hardware optimized for FHE operations, potentially fueling a new niche in the compute supply chain.
Ethical and regulatory frameworks around data privacy in AI become more complex and sophisticated as technical solutions advance.
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Read at arXiv cs.LG