# GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs

arXiv: https://arxiv.org/abs/2604.11659 · HTML: https://mlsanity.com/paper/2604.11659/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures](https://mlsanity.com/paper/1607.03250/) (2016)
- [When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control](https://mlsanity.com/paper/2008.08753/) (2020)
- [Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation](https://mlsanity.com/paper/2202.07253/) (2022)
- [Flexagon: A Multi-Dataflow Sparse-Sparse Matrix Multiplication Accelerator for Efficient DNN Processing](https://mlsanity.com/paper/2301.10852/) (2023)
- [Designing Efficient LLM Accelerators for Edge Devices](https://mlsanity.com/paper/2408.00462/) (2024)
- [HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity](https://mlsanity.com/paper/2305.12718/) (2023)
- [TensorFHE: Achieving Practical Computation on Encrypted Data Using GPGPU](https://mlsanity.com/paper/2212.14191/) (2022)
- [GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic Encryption](https://mlsanity.com/paper/2309.11001/) (2023)
- [FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs](https://mlsanity.com/paper/2507.04775/) (2025)
- [Accelerating convolutional neural network by exploiting sparsity on GPUs](https://mlsanity.com/paper/1909.09927/) (2019)
- [Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs](https://mlsanity.com/paper/2503.09184/) (2025)
- [Changing Base Without Losing Pace: A GPU-Efficient Alternative to MatMul in DNNs](https://mlsanity.com/paper/2503.12211/) (2025)
- [Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption](https://mlsanity.com/paper/2509.03024/) (2025)

## Abstract (arXiv)

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× and reduces the time complexity from cubic to semi-linear, demonstrating an improvement over existing FHE matmul implementations.