通过自适应优化提升量子联邦学习在异构客户端下的性能表现
New Insights on Unfolding and Fine-tuning Quantum Federated Learning
- 用深度展开技术让客户端自主调节学习率等超参数
- 实测准确率达90%，远超传统方法的55%
- 适合医疗基因分析等对精度要求高的量子应用
客户端异构性严重制约量子联邦学习（QFL）性能。为此，我们提出一种新方法，利用深度展开技术，使客户端能根据自身训练行为自主优化学习率、正则化因子等超参数。该动态适应机制有效缓解过拟合，在标准聚合方法失效的高度异构环境下仍保持稳健优化。通过在IBM量子硬件和Qiskit Aer模拟器上的实时训练验证，该框架实现约90%的准确率，显著优于传统方法的约55%。所提方法通过可学习的、感知收敛性的优化步骤，增强了诊断精度与预测建模能力，在基因表达分析与癌症检测等关键应用中表现优异。研究结果归因于深度展开框架内嵌的可学习优化步，兼顾了泛化能力。本工作解决了传统QFL的核心局限，推动其在医疗与基因组研究等复杂挑战中的应用。
原文摘要 · Abstract (English)
Client heterogeneity poses significant challenges to the performance of Quantum Federated Learning (QFL). To overcome these limitations, we propose a new approach leveraging deep unfolding, which enables clients to autonomously optimize hyperparameters, such as learning rates and regularization factors, based on their specific training behavior. This dynamic adaptation mitigates overfitting and ensures robust optimization in highly heterogeneous environments where standard aggregation methods often fail. Our framework achieves approximately 90% accuracy, significantly outperforming traditional methods, which typically yield around 55% accuracy, as demonstrated through real-time training on IBM quantum hardware and Qiskit Aer simulators. By developing self adaptive fine tuning, the proposed method proves particularly effective in critical applications such as gene expression analysis and cancer detection, enhancing diagnostic precision and predictive modeling within quantum systems. Our results are attributed to convergence-aware, learnable optimization steps intrinsic to the deep unfolded framework, which maintains the generalization. Hence, this study addresses the core limitations of conventional QFL, streamlining its applicability to any complex challenges such as healthcare and genomic research.
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