mlsanityData to 7 Oct 2026

Experimentally validated quantum-secure federated learning over a multi-user quantum network

arXiv 2501.12709 ↗First posted 22 Jan 2025quant-ph cs.AI cs.CRcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 36 of its 88 references that are in the map. First posted on arXiv 22 Jan 2025.

Builds on

36 of 88 references.

References · 36

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24 more
  1. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 74
  2. 2021QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computercited by 65
  3. 2020Federated Learning With Quantized Global Model Updatescited by 54
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  21. 2021Breaking the Rate-Loss Bound of Quantum Key Distribution with Asynchronous Two-Photon Interferencecited by 1
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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Quantum Federated Learning: Architectural Elements and Future Directionscited by 1
  2. 2025Quantum automated learning with provable and explainable trainabilitycited by 0
  3. 2025AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparingcited by 0
  4. 2025Emerging Paradigms for Securing Federated Learning Systemscited by 0
  5. 2025Adaptive Aggregation with Two Gains in QFLcited by 0