Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions

TL;DR

This paper explores the challenges of heterogeneity in quantum federated learning, analyzing its impact on training stability and proposing directions for developing robust, scalable solutions.

Contribution

It classifies heterogeneity in QFL, evaluates existing mitigation methods, and demonstrates a case study addressing quantum data and system variances.

Findings

Heterogeneity affects convergence and model performance in QFL.

Existing solutions have limitations in handling quantum heterogeneity.

A case study shows potential strategies for managing heterogeneity.

Abstract

Quantum federated learning (QFL) combines quantum computing and federated learning to enable decentralized model training while maintaining data privacy. QFL can improve computational efficiency and scalability by taking advantage of quantum properties such as superposition and entanglement. However, existing QFL frameworks largely focus on homogeneity among quantum \textcolor{black}{clients, and they do not account} for real-world variances in quantum data distributions, encoding techniques, hardware noise levels, and computational capacity. These differences can create instability during training, slow convergence, and reduce overall model performance. In this paper, we conduct an in-depth examination of heterogeneity in QFL, classifying it into two categories: data or system heterogeneity. Then we investigate the influence of heterogeneity on training convergence and model aggregation. We critically evaluate existing mitigation solutions, highlight their limitations, and give a case study that demonstrates the viability of tackling quantum heterogeneity. Finally, we discuss potential future research areas for constructing robust and scalable heterogeneous QFL frameworks.