FedQNN: Federated Learning using Quantum Neural Networks
arxiv(2024)
摘要
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.
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