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 required to continue. Every nonlinearity must therefore be approximated by an iterative method; each iteration increasing the number of multiplications. A higher iteration count buys precision but exhausts the available depth more frequently and thus triggers more bootstraps, which dominate latency. We introduce Homomorphic Encryption-Aware Training (HEAT),...
| Authors | Alessandro Zirilli, Davide Marincione, Evgenios M. Kornaropoulos et al. |
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| Primary category | cs.CR |
| Published | 1 September 2026 |
| Last updated at source | 30 September 2026 |
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