Personalized Quantum Federated Learning for Privacy Image Classification

TL;DR

This paper introduces a personalized quantum federated learning algorithm that enhances client model individuality for privacy image classification, achieving superior accuracy and privacy preservation, especially with imbalanced data distributions.

Contribution

It proposes a novel personalized quantum federated learning model with a dedicated personalized layer and a secure algorithm, improving performance over non-personalized methods in quantum image classification.

Findings

Achieves 100% server accuracy with 8 clients, outperforming non-personalized models.

Client accuracy is 2.9% higher on average with personalization.

Eliminates additional local training while safeguarding privacy.

Abstract

Quantum federated learning has brought about the improvement of privacy image classification, while the lack of personality of the client model may contribute to the suboptimal of quantum federated learning. A personalized quantum federated learning algorithm for privacy image classification is proposed to enhance the personality of the client model in the case of an imbalanced distribution of images. First, a personalized quantum federated learning model is constructed, in which a personalized layer is set for the client model to maintain the personalized parameters. Second, a personalized quantum federated learning algorithm is introduced to secure the information exchanged between the client and server.Third, the personalized federated learning is applied to image classification on the FashionMNIST dataset, and the experimental results indicate that the personalized quantum federated learning algorithm can obtain global and local models with excellent performance, even in situations where local training samples are imbalanced. The server's accuracy is 100% with 8 clients and a distribution parameter of 100, outperforming the non-personalized model by 7%. The average client accuracy is 2.9% higher than that of the non-personalized model with 2 clients and a distribution parameter of 1. Compared to previous quantum federated learning algorithms, the proposed personalized quantum federated learning algorithm eliminates the need for additional local training while safeguarding both model and data privacy.It may facilitate broader adoption and application of quantum technologies, and pave the way for more secure, scalable, and efficient quantum distribute machine learning solutions.