arXiv:2610.32065v1 Announce Type: new Abstract: Nonlinear activation evaluation remains a major bottleneck in neural inference under fully homomorphic encryption (FHE). FHE supports efficient addition and multiplication, but lacks native operations for exponential functions, reciprocals, and value-dependent selection. High-degree polynomial approximations on fixed intervals are therefore commonly used to replace nonlinear activations.
SPA: Structure-Preserving Approximation of Gated Activations under FHE
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