FedQNN: Federated Learning using Quantum Neural Networks

arXiv:2403.10861v1 Announce Type: cross
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 …

abstract arxiv cs.et cs.lg data data privacy distributed domain explore federated learning framework information machine machine learning machine learning models networks neural networks privacy qml quant-ph quantum quantum neural networks study training type via