In this study, we explore the innovative domain of Quantum Federated Learning
(QFL) as a framework for training Quantum Machine Learning (QML) models via
distributed networks. Conventional machine learning models frequently grapple
with issues about data privacy and the exposure of sensitive information. Our
proposed Federated Quantum Neural Network (FedQNN) framework emerges as a
cutting-edge solution, integrating the singular characteristics of QML with the
principles of classical federated learning. This work thoroughly investigates
QFL, underscoring its capability to secure data handling in a distributed
environment and facilitate cooperative learning without direct data sharing.
Our research corroborates the concept through experiments across varied
datasets, including genomics and healthcare, thereby validating the versatility
and efficacy of our FedQNN framework. The results consistently exceed 86%
accuracy across three distinct datasets, proving its suitability for conducting
various QML tasks. Our research not only identifies the limitations of
classical paradigms but also presents a novel framework to propel the field of
QML into a new era of secure and collaborative innovation.

本研究探索了量子联邦学习作为一个框架，通过分布式网络来训练量子机器学习模型的创新领域。我们提出的联邦量子神经网络框架是一种前沿解决方案，将量子机器学习的独特特性与经典联邦学习的原理融合在一起。通过在不同数据集上进行的实验证明，我们的 FedQNN 框架具有很强的适应性和功效，可以在分布式环境中安全处理数据并促进合作学习，结果在三个不同数据集上始终保持超过 86% 的准确率，证明了它在进行各种量子机器学习任务上的适用性。我们的研究不仅确定了传统范式的局限性，还提出了一个新的框架，推动量子机器学习领域进入一个新时代的安全和协作创新。