FedQNN: Federated Learning using Quantum Neural Networks

Abstract

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.

Accepted for presentation at IJCNN 2024

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Cited by in corpus (11)

• QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
• Next-Generation Quantum Neural Networks: Enhancing Efficiency, Security, and Privacy
• Simulation of a Variational Quantum Perceptron using Grover's Algorithm
• A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
• When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
• LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks
• Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach
• Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection
• MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption
• Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection
• AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing