GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs

• URL: http://arxiv.org/abs/2604.11659v1
• Date: Mon, 13 Apr 2026 16:09:26 GMT
• Title: GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
• Abstract summary: homomorphic encryption (FHE) has attracted significant attention as both a cryptographic primitive and a systems challenge.<n>We propose a new optimized method that improves the runtime and complexity of ciphertext matmul by using FIDESlib, a recent open-source FHE library designed specifically for GPUs.
• Score: 1.8933299988942567
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Fully homomorphic encryption (FHE) has recently attracted significant attention as both a cryptographic primitive and a systems challenge. Given the latest advances in accelerated computing, FHE presents a promising opportunity for progress, with applications ranging from machine learning to information security. We target the most computationally intensive operation in deep neural networks from a hardware perspective, matrix multiplication (matmul), and adapt it for execution on AMD GPUs. We propose a new optimized method that improves the runtime and complexity of ciphertext matmul by using FIDESlib, a recent open-source FHE library designed specifically for GPUs. By exploiting sparsity in both operands, our sparse matmul implementation outperforms its CPU counterpart by up to $3.0\times$ and reduces the time complexity from cubic to semi-linear, demonstrating an improvement over existing FHE matmul implementations.

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