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A distributed framework termed Federated Quantum Neural Network, or FedQNN, trains quantum machine learning models without requiring direct data sharing between participating nodes. Conventional machine learning frequently encounters challenges regarding data privacy and the exposure of sensitive information. FedQNN addresses these limitations by integrating quantum machine learning techniques with the principles of classical federated learning, enabling secure, cooperative training across decentralised environments. Experimental evaluations on three distinct datasets, including applications in genomics and healthcare, demonstrated the adaptability and effectiveness of the system. Across all three datasets, the framework consistently achieved predictive accuracy exceeding 86 per cent, confirming its suitability for carrying out varied quantum machine learning tasks while preserving data privacy.
Protecting confidential records is a central challenge when training artificial intelligence models across organisations. By enabling quantum neural networks to learn collaboratively without exchanging underlying data, this approach offers a way to train sophisticated models on sensitive information, such as patient health records and genomic data, without compromising individual privacy.
The framework is relevant to organisations handling sensitive data, particularly in healthcare and genomics, seeking to collaborate on predictive modelling. Given that the findings are based on experimental validation across three datasets, this work represents early-stage, applied research. Practical commercial deployment would require further development on operational quantum hardware and distributed enterprise networks.
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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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DOI: 10.1109/ijcnn60899.2024.10651123
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