semantic_scholarProceedings on Privacy Enhancing Technologies
Training TFHE-Based Neural Networks with Approximated Floating-Point Arithmetic
Emanuel Nicoletti, Fabrizio Pittorino, Alessandro Falcetta, Luca Colombo, Manuel Roveri
TL;DR: This work presents a framework that enables approximate floating-point training within TFHE by reinterpreting IEEE 754 representations as encrypted integers and operating on them with redesigned arithmetic, and performs the first fully encrypted training of a small-scale CNN under TFHE.
Training neural networks under Torus Fully Homomorphic Encryption (TFHE) is severely constrained by the native restriction of the scheme to boolean and integer arithmetic, forcing prior work to rely on quantized integer pipelines limited to shallow MLPs. We present a framework th…