SOURCE-LINKED INTELLIGENCE
HEAT: Faster Fully Homomorphic Inference via Approximations-Weights Co-Adaptation
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 uses multiplications. A higher iteration count buys precision but exhausts the available depth faster and triggers more bootstraps, which dominate la
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T18:02:02.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.