mlsanityData to 7 Oct 2026

Privacy-preserving quantum federated learning via gradient hiding

arXiv 2312.04447 ↗First posted 7 Dec 2023quant-ph cs.CR cs.DCcited by 15

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Abstract (arXiv)

Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum nodes. Meanwhile, the critical concern of privacy within distributed computing protocols remains a significant challenge, particularly in standard classical federated learning (FL) scenarios where data of participating clients is susceptible to leakage via gradient inversion attacks by the server. This paper presents innovative quantum protocols with quantum communication designed to address the FL problem, strengthen privacy measures, and optimize communication efficiency. In contrast to previous works that leverage expressive variational quantum circuits or differential privacy techniques, we consider gradient information concealment using quantum states and propose two distinct FL protocols, one based on private inner-product estimation and the other on incremental learning. These protocols offer substantial advancements in privacy preservation with low communication resources, forging a path toward efficient quantum communication-assisted FL protocols and contributing to the development of secure distributed quantum machine learning, thus addressing critical privacy concerns in the quantum computing era.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 26 of its 62 references that are in the map. First posted on arXiv 7 Dec 2023.

Builds on

26 of 62 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2019Deep Leakage from Gradientscited by 1,004
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2019Federated Machine Learning: Concept and Applicationscited by 409
  6. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  7. 2019Parameterized quantum circuits as machine learning modelscited by 330
  8. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  9. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  10. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  11. 2020LDP-Fed: Federated Learning with Local Differential Privacycited by 129
  12. 2020R-GAP: Recursive Gradient Attack on Privacycited by 95
14 more
  1. 2021Federated Quantum Machine Learningcited by 82
  2. 2020Quantum noise protects quantum classifiers against adversariescited by 68
  3. 2021Quantum federated learning through blind quantum computingcited by 44
  4. 2022No Free Lunch Theorem for Security and Utility in Federated Learningcited by 39
  5. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  6. 2023Towards Quantum Federated Learningcited by 20
  7. 2020Towards a Distributed Quantum Computing Ecosystemcited by 11
  8. 2016Private quantum computation: An introduction to blind quantum computing and related protocolscited by 10
  9. 2022Distributed Quantum Computing: a Surveycited by 8
  10. 2023Expressive variational quantum circuits provide inherent privacy in federated learningcited by 8
  11. 2023Blind quantum machine learning with quantum bipartite correlatorcited by 8
  12. 2022Enhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategiescited by 5
  13. 2018Quantum Internet: Networking Challenges in Distributed Quantum Computingcited by 4
  14. 2019Secure Multi-party Quantum Computation with a Dishonest Majoritycited by 2

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Citing papers per year: 2024: 5; 2025: 8; 2026: 2.

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  1. 2024Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcarecited by 7
  2. 2024Quantum delegated and federated learning via quantum homomorphic encryptioncited by 7
  3. 2025Experimentally validated quantum-secure federated learning over a multi-user quantum networkcited by 5
  4. 2025When Federated Learning Meets Quantum Computing: Survey and Research Opportunitiescited by 5
  5. 2025Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomycited by 4
  6. 2024Prospects of Privacy Advantage in Quantum Machine Learningcited by 3
  7. 2025Adversarial Robustness of Partitioned Quantum Classifierscited by 2
  8. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  9. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
  10. 2025A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networkscited by 1
  11. 2024Federated learning with distributed fixed design quantum chips and quantum channelscited by 0
  12. 2024Enhancing Privacy in Federated Learning through Quantum Teleportation Integrationcited by 0
3 more
  1. 2025Emerging Paradigms for Securing Federated Learning Systemscited by 0
  2. 2026Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approachcited by 0
  3. 2026Quantum ring all-reduce: communication and privacy advantages for distributed learningcited by 0