Quantum Federated Learning for Image Classification

Leo Sünkel, Philipp Altmann, Michael Kölle, Thomas Gabor

2024

Abstract

Federated learning is a technique in classical machine learning in which a global model is collectively trained by a number of independent clients, each with their own datasets. Using this learning method, clients are not required to reveal their dataset as it remains local; clients may only exchange parameters with each other. As the interest in quantum computing and especially quantum machine learning is steadily increasing, more concepts and approaches based on classical machine learning principles are being applied to the respective counterparts in the quantum domain. Thus, the idea behind federated learning has been transferred to the quantum realm in recent years. In this paper, we evaluate a straightforward approach to quantum federated learning using the widely used MNIST dataset. In this approach, we replace a classical neural network with a variational quantum circuit, i.e., the global model as well as the clients are trainable quantum circuits. We run three different experiments which differ in number of clients and data-subsets used. Our results demonstrate that basic principles of federated learning can be applied to the quantum domain while still achieving acceptable results. However, they also illustrate that further research is required for scenarios with increasing number of clients.

Paper Citation

in Harvard Style

Sünkel L., Altmann P., Kölle M. and Gabor T. (2024). Quantum Federated Learning for Image Classification. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 936-942. DOI: 10.5220/0012421200003636

in Bibtex Style

@conference{icaart24,
author={Leo Sünkel and Philipp Altmann and Michael Kölle and Thomas Gabor},
title={Quantum Federated Learning for Image Classification},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={936-942},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012421200003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Quantum Federated Learning for Image Classification
SN - 978-989-758-680-4
AU - Sünkel L.
AU - Altmann P.
AU - Kölle M.
AU - Gabor T.
PY - 2024
SP - 936
EP - 942
DO - 10.5220/0012421200003636
PB - SciTePress