论文标题
通过量子安全汇总联合学习
Federated Learning with Quantum Secure Aggregation
论文作者
论文摘要
本文说明了一种新颖的量子安全聚合（QSA）方案，该方案旨在为联合学习提供高度安全有效的局部模型参数聚合。该方案可通过利用量子位即表示模型参数来保护私人模型参数免于被披露给半冬季攻击者。提出的安全机制可确保任何尝试窃听私人模型参数的尝试，都可以立即检测并停止。就通过纠缠量子位的传输和聚合模型参数的低计算复杂性而言，该方案也有效。拟议的QSA方案的好处在水平联合学习环境中展示，其中考虑了集中式和分散的体系结构。从经验上证明，所提出的QSA可以很容易地应用于汇总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QSA方案的多功能性。相对于单个参与者获得的本地模型，全局模型的性能已提高到各种范围，而没有向半honest对手披露私人模型参数。
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.
