FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs

2025-07-07

AI Summary

To address the insufficient server-side performance of the CKKS scheme in OpenFHE for cloud deployment, this paper introduces the first GPU-accelerated library fully interoperable with OpenFHE. Our method designs highly optimized CUDA kernels implementing full-featured CKKS primitives—including bootstrapping—and incorporates a hardware abstraction layer and a multi-GPU scalable architecture, alongside a robust benchmarking framework. Evaluation shows ≥70× faster bootstrapping compared to AVX-optimized OpenFHE, and consistent outperformance over the state-of-the-art open-source library Phantom across diverse GPU platforms. These contributions significantly enhance homomorphic computation efficiency in privacy-preserving machine learning-as-a-service (MLaaS) and similar cloud-based applications. The library provides a high-performance, interoperable, and production-ready open-source infrastructure for practical fully homomorphic encryption (FHE) cloud deployment.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Applications of cryptography
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Word-wise Fully Homomorphic Encryption (FHE) schemes, such as CKKS, are gaining significant traction due to their ability to provide post-quantum-resistant, privacy-preserving approximate computing; an especially desirable feature in Machine-Learning-as-a-Service (MLaaS) cloud-computing paradigms. OpenFHE is a leading CPU-based FHE library with robust CKKS operations, but its server-side performance is not yet sufficient for practical cloud deployment. As GPU computing becomes more common in data centers, many FHE libraries are adding GPU support. However, integrating an efficient GPU backend into OpenFHE is challenging. While OpenFHE uses a Hardware Abstraction Layer (HAL), its flexible architecture sacrifices performance due to the abstraction layers required for multi-scheme and multi-backend compatibility. In this work, we introduce FIDESlib, the first open-source server-side CKKS GPU library that is fully interoperable with well-established client-side OpenFHE operations. Unlike other existing open-source GPU libraries, FIDESlib provides the first implementation featuring heavily optimized GPU kernels for all CKKS primitives, including bootstrapping. Our library also integrates robust benchmarking and testing, ensuring it remains adaptable to further optimization. Furthermore, its software architecture is designed to support extensions to a multi-GPU backend for enhanced acceleration. Our experiments across various GPU systems and the leading open-source CKKS library to date, Phantom, show that FIDESlib offers superior performance and scalability. For bootstrapping, FIDESlib achieves no less than 70x speedup over the AVX-optimized OpenFHE implementation.

Problem

• Enhancing CKKS performance on GPUs for cloud deployment
• Overcoming OpenFHE's CPU limitations with optimized GPU support
• Ensuring interoperability between server-side GPU and client-side CPU operations

Innovation

• First open-source CKKS GPU library
• Optimized GPU kernels for CKKS
• Supports multi-GPU backend extensions