发表机构
Deakin University（迪肯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对量子联邦学习在异构数据等因素下聚合不稳定及周期角参数平均失效的问题，提出自洽中点聚合方法，结合QoS加权等技术，经实验验证可提升稳定性并保持准确率。
AI 中文摘要
量子联邦学习（QFL）使客户端能够在不共享私有数据的情况下训练量子神经网络（QNN）模型。我们发现，在异构数据、不可靠通信、可变保真度、延迟以及量子硬件噪声的影响下，QFL中的聚合过程不稳定；此外，由于部分QNN参数为周期角，欧氏平均法往往无法捕捉其固有动态，这使得QFL面临显著挑战。我们提出一种新颖的自洽中点聚合方法，用于稳定QFL的设计与实现，该方法结合了感知服务质量（QoS）的客户端加权、循环参数聚合以及基于有界中点的更新控制。我们开展了多项角度测试和IBM真实量子机器实验进行验证，结果证实了我们的方法；在医疗和金融数据集上的大量评估与实验表明，该方法稳定性提升、波动性降低，且具有竞争力的准确率。
英文摘要
Quantum federated learning (QFL) enables clients to train quantum neural network (QNN) models without sharing private data. We find that aggregation in QFL is unstable under heterogeneous data, unreliable communication, variable fidelity, latency, and quantum hardware noise. Moreover, QFL is non-trivially challenging because several QNN parameters are periodic angles, where Euclidean averaging often fails to capture the inherent dynamics. We develop a novel self-consistent midpoint aggregation method for stable QFL design and implementation. We combine QoS-aware client weighting, circular parameter aggregation, and bounded midpoint-based update control. We perform several angular tests and IBM real Quantum machines experiments for validation confirming our approach. Extensive evaluations and experiments on medical and financial datasets show improved stability, lower volatility, and competitive accuracy.
