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\section{Related Work}
\label{sec:related}
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\textbf{CrypTen}~\cite{crypten} and \textbf{TenSEAL}~\cite{tenseal} provide
FHE wrappers for PyTorch tensors but lack GPU acceleration and production
serving infrastructure.
\textbf{Concrete ML}~\cite{concreteml} by Zama compiles scikit-learn models to
FHE circuits using TFHE. Our system extends this approach with CKKS support for
neural networks and GPU acceleration via Candle.
\textbf{Jin}~\cite{hanzojin} handles multimodal routing for Hanzo models; FHE
inference integrates as an optional encrypted execution backend for
privacy-sensitive requests.
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