Federated learning is essential for decentralized, privacy-preserving model training in the data-driven era. Quantum-enhanced federated learning leverages quantum resources to address privacy and scalability challenges, offering security and efficiency advantages beyond classical methods. However, practical and scalable frameworks addressing privacy concerns in the quantum computing era remain undeveloped. Here, we propose a practical quantum federated learning framework on quantum networks, utilizing distributed quantum secret keys to protect local model updates and enable secure aggregation with information-theoretic security. We experimentally validate our framework on a 4-client quantum network with a scalable structure. Extensive numerical experiments on both quantum and classical datasets show that adding a quantum client significantly enhances the trained global model's ability to classify multipartite entangled and non-stabilizer quantum datasets. Simulations further demonstrate scalability to 200 clients with classical models trained on the MNIST dataset, reducing communication costs by $75\%$ through advanced model compression techniques and achieving rapid training convergence. Our work provides critical insights for building scalable, efficient, and quantum-secure machine learning systems for the coming quantum internet era.

本研究解决了量子计算时代隐私保护和可扩展性方面的挑战，提出了一种实用的量子联邦学习框架。通过利用分布式量子秘密密钥来保护本地模型更新，实验验证显示该框架在分类量子数据集方面显著提高了全球模型的能力，并通过先进的模型压缩技术减少了75%的通信成本，展示了其在量子互联网时代构建高效安全机器学习系统的潜力。

实用量子联邦学习及其实验验证