Scaling Trust in Quantum Federated Learning: A Multi-Protocol Privacy Design
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911299676405760 |
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| author | Gurung, Dev Pokhrel, Shiva Raj |
| author_facet | Gurung, Dev Pokhrel, Shiva Raj |
| contents | Quantum Federated Learning (QFL) promises to revolutionize distributed machine learning by combining the computational power of quantum devices with collaborative model training. Yet, privacy of both data and models remains a critical challenge. In this work, we propose a privacy-preserving QFL framework where a network of $n$ quantum devices trains local models and transmits them to a central server under a multi-layered privacy protocol. Our design leverages Singular Value Decomposition (SVD), Quantum Key Distribution (QKD), and Analytic Quantum Gradient Descent (AQGD) to secure data preparation, model sharing, and training stages. Through theoretical analysis and experiments on contemporary quantum platforms and datasets, we demonstrate that the framework robustly safeguards data and model confidentiality while maintaining training efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scaling Trust in Quantum Federated Learning: A Multi-Protocol Privacy Design Gurung, Dev Pokhrel, Shiva Raj Cryptography and Security Quantum Federated Learning (QFL) promises to revolutionize distributed machine learning by combining the computational power of quantum devices with collaborative model training. Yet, privacy of both data and models remains a critical challenge. In this work, we propose a privacy-preserving QFL framework where a network of $n$ quantum devices trains local models and transmits them to a central server under a multi-layered privacy protocol. Our design leverages Singular Value Decomposition (SVD), Quantum Key Distribution (QKD), and Analytic Quantum Gradient Descent (AQGD) to secure data preparation, model sharing, and training stages. Through theoretical analysis and experiments on contemporary quantum platforms and datasets, we demonstrate that the framework robustly safeguards data and model confidentiality while maintaining training efficiency. |
| title | Scaling Trust in Quantum Federated Learning: A Multi-Protocol Privacy Design |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2512.03358 |