标题： FL-QDSNNs：具有量子动态脉冲神经网络的联邦学习 显示英文标题

标题： FL-QDSNNs: Federated Learning with Quantum Dynamic Spiking Neural Networks

摘要： 本文介绍了联邦学习-量子动态脉冲神经网络（FL-QDSNNs）框架，这是一种专门设计用于解决分布式学习系统中重大挑战的创新方法，例如在保持高精度的同时确保隐私。 我们框架的核心是一个针对量子脉冲神经网络（QSNNs）中量子门激活的新动态阈值机制，该机制模仿经典激活函数，同时独特地利用量子操作来提升计算性能。 该机制对于应对动态变化的数据分布中的典型性能波动至关重要，这是传统QSNNs应用中的常见挑战。 通过在Iris、digits和乳腺癌等数据集上的广泛测试验证，我们的FL-QDSNNs框架已显示出优越的准确性，在Iris数据集上的准确率高达94%，并显著优于现有的量子联邦学习（QFL）方法。 我们的结果表明，我们的FL-QDSNNs框架在客户端数量方面具有可扩展性，提供了改进的学习能力，并代表了一种稳健的解决方案，以应对新兴量子硬件和复杂QSNNs训练协议带来的隐私和效率限制。 通过从根本上提升QSNNs在现实世界分布式环境中的操作能力，该框架有可能重新定义量子计算在敏感和关键领域的应用格局，确保增强的数据安全性和系统性能。 显示英文摘要

摘要： This paper introduces the Federated Learning-Quantum Dynamic Spiking Neural Networks (FL-QDSNNs) framework, an innovative approach specifically designed to tackle significant challenges in distributed learning systems, such as maintaining high accuracy while ensuring privacy. Central to our framework is a novel dynamic threshold mechanism for activating quantum gates in Quantum Spiking Neural Networks (QSNNs), which mimics classical activation functions while uniquely exploiting quantum operations to enhance computational performance. This mechanism is essential for tackling the typical performance variability across dynamically changing data distributions, a prevalent challenge in conventional QSNNs applications. Validated through extensive testing on datasets including Iris, digits, and breast cancer, our FL-QDSNNs framework has demonstrated superior accuracies-up to 94% on the Iris dataset and markedly outperforms existing Quantum Federated Learning (QFL) approaches. Our results reveal that our FL-QDSNNs framework offers scalability with respect to the number of clients, provides improved learning capabilities, and represents a robust solution to privacy and efficiency limitations posed by emerging quantum hardware and complex QSNNs training protocols. By fundamentally advancing the operational capabilities of QSNNs in real-world distributed environments, this framework can potentially redefine the application landscape of quantum computing in sensitive and critical sectors, ensuring enhanced data security and system performance.