Experimentally validated quantum-secure federated learning over a multi-user quantum network
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914570074849280 |
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| author | Liu, Zhi-Ping Cao, Xiao-Yu Liu, Hao-Wen Sun, Xiao-Ran Bao, Yu Shen, Jian-Yu Lu, Yu-Shuo Yin, Hua-Lei Chen, Zeng-Bing |
| author_facet | Liu, Zhi-Ping Cao, Xiao-Yu Liu, Hao-Wen Sun, Xiao-Ran Bao, Yu Shen, Jian-Yu Lu, Yu-Shuo Yin, Hua-Lei Chen, Zeng-Bing |
| contents | Federated learning enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum federated learning (QFL) offers a promising path towards enhanced security and efficiency. However, a practical and experimentally validated QFL protocol utilizing near-term quantum techniques to address data privacy has been lacking. Here we present QuNetQFL, a QFL protocol implemented on quantum networks, in which local model updates are masked with distributed quantum secret keys, offering information-theoretic security during aggregation. We experimentally validate the protocol on a four-client quantum network and benchmark its performance using the generated keys on quantum and real-world datasets. Adding a single quantum client significantly improves global accuracy for classifying multipartite entangled and non-stabilizer quantum datasets. For language tasks, we apply QuNetQFL to sentiment analysis by federated fine-tuning of a hybrid classical-quantum language model, achieving comparable and robust performance in simulation and on real quantum hardware. Large-scale simulations further demonstrate scalability to 200 clients for handwritten-digit recognition, with rapid convergence and a $75\%$ reduction in communication cost via model compression. Our work establishes a practical and scalable route to quantum-secure federated learning for the emerging quantum internet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimentally validated quantum-secure federated learning over a multi-user quantum network Liu, Zhi-Ping Cao, Xiao-Yu Liu, Hao-Wen Sun, Xiao-Ran Bao, Yu Shen, Jian-Yu Lu, Yu-Shuo Yin, Hua-Lei Chen, Zeng-Bing Quantum Physics Artificial Intelligence Cryptography and Security Distributed, Parallel, and Cluster Computing Federated learning enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum federated learning (QFL) offers a promising path towards enhanced security and efficiency. However, a practical and experimentally validated QFL protocol utilizing near-term quantum techniques to address data privacy has been lacking. Here we present QuNetQFL, a QFL protocol implemented on quantum networks, in which local model updates are masked with distributed quantum secret keys, offering information-theoretic security during aggregation. We experimentally validate the protocol on a four-client quantum network and benchmark its performance using the generated keys on quantum and real-world datasets. Adding a single quantum client significantly improves global accuracy for classifying multipartite entangled and non-stabilizer quantum datasets. For language tasks, we apply QuNetQFL to sentiment analysis by federated fine-tuning of a hybrid classical-quantum language model, achieving comparable and robust performance in simulation and on real quantum hardware. Large-scale simulations further demonstrate scalability to 200 clients for handwritten-digit recognition, with rapid convergence and a $75\%$ reduction in communication cost via model compression. Our work establishes a practical and scalable route to quantum-secure federated learning for the emerging quantum internet. |
| title | Experimentally validated quantum-secure federated learning over a multi-user quantum network |
| topic | Quantum Physics Artificial Intelligence Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2501.12709 |