HEAT: Faster Fully Homomorphic Inference via Approximations-Weights Co-Adaptation
HEAT co-optimises model weights and FHE-friendly approximations to slash homomorphic inference latency—potentially making privacy-preserving LLM queries more practical for regulated industries.
Summary written by editorial AI · Source link below
arXiv:2609.01730v1 Announce Type: new Abstract: Fully homomorphic encryption (FHE) allows a server to run a language model directly on encrypted user prompts, but current approaches remain prohibitively slow. Ciphertexts natively support only addition, multiplication, and rotation, and multiplications may be composed only to a bounded depth before a costly bootstrapping operation is needed to continue. Every nonlinearity must therefore be approximated by an iterative method, and each iteration
Editorial Analysis
Faster FHE inference could unlock compliant processing of sensitive data in EU-regulated sectors where plaintext cloud compute remains a legal or risk barrier.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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