Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML

Siddhant Dutta, Pavana P Karanth, Pedro Maciel Xavier, Iago Leal de Freitas, Nouhaila Innan, Sadok Ben Yahia, Muhammad Shafique, David E. Bernal Neira
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Abstract

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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利用量子计算和全同态加密进行联合学习:保护隐私的多语言计算的新计算范式转变
由机器学习模型驱动的产品的广泛部署正在引发全球对数据隐私和信息安全的关注。为了解决这个问题,人们首次提出了联盟学习(Federated Learning),作为传统方法的隐私保护替代方案,允许多个学习客户端共享模型知识,而不泄露私人数据。被称为全同态加密(FHE)的补充方法是一种量子安全加密系统,可在加密权重上执行操作。然而,在实际应用中实施这类机制往往会带来巨大的计算开销,并可能暴露潜在的安全威胁。新的计算模式,如模拟、量子和专用数字硬件,为实现保护隐私的机器学习系统,同时提高安全性和减少性能损失提供了机会。本研究将 FHE 方案应用于集成了经典层和量子层的联合学习神经网络架构,从而实现了这些想法。
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