破解量子联邦学习中的设备差异难题，提升模型稳定性和收敛速度。
Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions
- 区分数据与系统异构性，系统分析其对训练的影响。
- 实证发现异构性导致收敛变慢、性能下降。
- 提出针对性解决方案，适合研究量子联邦学习的学者参考。
量子联邦学习（QFL）结合量子计算与联邦学习，实现去中心化模型训练并保障数据隐私。借助量子叠加与纠缠等特性，QFL可提升计算效率和可扩展性。然而，现有框架多假设客户端同质，忽视了真实场景中量子数据分布、编码方式、硬件噪声水平及算力差异等异构性。这些差异会引发训练不稳定、收敛缓慢，降低整体模型性能。本文深入剖析QFL中的异构性，将其分为数据异构与系统异构两类，并研究其对训练收敛与模型聚合的影响。我们评估现有缓解方案的局限性，通过案例研究验证解决量子异构性的可行性。最后，展望构建鲁棒、可扩展异构QFL框架的未来方向。
原文摘要 · Abstract (English)
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.
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