将量子计算与全同态加密结合，实现更安全高效的联邦学习。
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML
- 用全同态加密保护联邦学习中的模型权重
- 融合经典与量子层神经网络，提升隐私保护能力
- 适合关注隐私计算与量子机器学习的研究者
基于机器学习的产品广泛部署，引发全球对数据隐私和信息安全的担忧。为应对这一问题，联邦学习作为隐私保护替代方案被提出，使多个客户端可在不共享私有数据的情况下共同更新模型知识。与此同时，全同态加密（FHE）是一种抗量子攻击的密码系统，可对加密权重执行操作。然而，实际应用中此类机制常伴随显著计算开销，并可能引入安全风险。模拟、量子及专用数字硬件等新型计算范式为实现更安全且性能损耗更低的隐私保护机器学习系统提供了可能。本文通过将FHE方案应用于集成经典与量子层的联邦学习神经网络架构，实现了这一设想。
原文摘要 · Abstract (English)
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.
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