This article illustrates a novel Quantum Secure Aggregation (QSA) scheme that is designed to provide highly secure and efficient aggregation of local model parameters for federated learning. The scheme is secure in protecting private model parameters from being disclosed to semi-honest attackers by utilizing quantum bits i.e. qubits to represent model parameters. The proposed security mechanism ensures that any attempts to eavesdrop private model parameters can be immediately detected and stopped. The scheme is also efficient in terms of the low computational complexity of transmitting and aggregating model parameters through entangled qubits. Benefits of the proposed QSA scheme are showcased in a horizontal federated learning setting in which both a centralized and decentralized architectures are taken into account. It was empirically demonstrated that the proposed QSA can be readily applied to aggregate different types of local models including logistic regression (LR), convolutional neural networks (CNN) as well as quantum neural network (QNN), indicating the versatility of the QSA scheme. Performances of global models are improved to various extents with respect to local models obtained by individual participants, while no private model parameters are disclosed to semi-honest adversaries.

翻译：本文阐述了一种新颖的量子安全聚合（QSA）方案，该方案旨在为联邦学习提供高度安全且高效的局部模型参数聚合。该方案通过利用量子比特（即qubits）表示模型参数，从而保护私有模型参数不被半诚实攻击者泄露。所提出的安全机制确保任何窃听私有模型参数的企图都能被立即检测并阻止。此外，该方案在通过纠缠量子比特传输和聚合模型参数时具有较低的计算复杂度，因而具有高效性。所提出的QSA方案的优势在水平联邦学习场景中得到展示，其中考虑了集中式和分布式两种架构。实验证明，所提出的QSA可轻松应用于聚合不同类型的局部模型，包括逻辑回归（LR）、卷积神经网络（CNN）以及量子神经网络（QNN），表明了QSA方案的通用性。与单个参与者获得的局部模型相比，全局模型的性能在不同程度上得到提升，同时私有模型参数未向半诚实攻击者泄露。