The widespread deployment of products powered by machine learning models is raising concerns around data privacy and information security worldwide. To address this issue, Federated Learning was first proposed as a privacy-preserving alternative to conventional methods that allow multiple learning clients to share model knowledge without disclosing private data. A complementary approach known as Fully Homomorphic Encryption (FHE) is a quantum-safe cryptographic system that enables operations to be performed on encrypted weights. However, implementing mechanisms such as these in practice often comes with significant computational overhead and can expose potential security threats. Novel computing paradigms, such as analog, quantum, and specialized digital hardware, present opportunities for implementing privacy-preserving machine learning systems while enhancing security and mitigating performance loss. This work instantiates these ideas by applying the FHE scheme to a Federated Learning Neural Network architecture that integrates both classical and quantum layers.

本研究针对传统机器学习方法中数据隐私和安全性问题进行探讨，提出将全同态加密和量子计算相结合的联邦学习新架构。该方法能够在保障数据隐私的同时，提升计算效率，并减少安全隐患。研究结果显示，此新架构在性能与安全性之间达成良好平衡，具有广泛的应用潜力。

量子计算与全同态加密的联邦学习：隐私保护机器学习中的新计算范式转变