用量子技术压缩联邦学习模型参数，提升边缘设备效率
QFed: Parameter-Compact Quantum-Classical Federated Learning
- 结合量子计算与联邦学习，通过多对数因子减少模型参数
- 在FashionMNIST上使VGG类模型参数量减少77.6%且精度相当
- 适合资源受限的边缘设备部署，推动隐私保护下的协同建模
医疗、金融、科研等领域需在遵守严格隐私、监管和主权要求的前提下，从分布式孤岛数据中提取集体智能。联邦学习（FL）可在不共享原始数据的情况下实现协作建模，但面临统计异质性、系统多样性及复杂模型带来的计算负担。本研究探索量子辅助联邦学习的潜力，可将经典模型参数量降低至多对数级别，从而减轻训练开销。为此，我们提出QFed——一种面向边缘设备网络的量子增强联邦学习框架。在广泛使用的FashionMNIST数据集上进行评估，实验结果表明，QFed在可扩展环境中使VGG类模型的参数量减少77.6%，同时保持与经典方法相当的准确率。这些结果表明，在联邦学习中引入量子计算，有望增强边缘设备的建模能力。
原文摘要 · Abstract (English)
Organizations and enterprises across domains such as healthcare, finance, and scientific research are increasingly required to extract collective intelligence from distributed, siloed datasets while adhering to strict privacy, regulatory, and sovereignty requirements. Federated Learning (FL) enables collaborative model building without sharing sensitive raw data, but faces growing challenges posed by statistical heterogeneity, system diversity, and the computational burden from complex models. This study examines the potential of quantum-assisted federated learning, which could cut the number of parameters in classical models by polylogarithmic factors and thus lessen training overhead. Accordingly, we introduce QFed, a quantum-enabled federated learning framework aimed at boosting computational efficiency across edge device networks. We evaluate the proposed framework using the widely adopted FashionMNIST dataset. Experimental results show that QFed achieves a 77.6% reduction in the parameter count of a VGG-like model while maintaining an accuracy comparable to classical approaches in a scalable environment. These results point to the potential of leveraging quantum computing within a federated learning context to strengthen FL capabilities of edge devices.
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