mlsanityData to 14 Oct 2026

When Federated Learning Meets Quantum Computing: Survey and Research Opportunities

arXiv 2504.08814 ↗First posted 9 Apr 2025cs.DC cs.ET cs.LGcited by 5

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Abstract (arXiv)

Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of the QFL research. This work explores key developments and integration strategies, along with the impact of QC on FL, keeping a sharp focus on hybrid quantum-classical approaches. The paper offers an in-depth understanding of how the strengths of QC, such as gradient hiding, state entanglement, quantum key distribution, quantum security, and quantum-enhanced differential privacy, have been integrated into FL to ensure the privacy of participants in an enhanced, fast, and secure framework. Finally, this study proposes potential future directions to address the identified research gaps and challenges, aiming to inspire faster and more secure QFL models for practical use.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 56 of its 222 references that are in the map. First posted on arXiv 9 Apr 2025.

Builds on

56 of 222 references.

References · 56

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2020Adaptive Federated Optimizationcited by 1,046
  5. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
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  10. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  11. 2018Barren plateaus in quantum neural network training landscapescited by 630
  12. 2020Power of data in quantum machine learningcited by 338
38 more
  1. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
  2. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  3. 2019Asynchronous Federated Optimizationcited by 267
  4. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 233
  5. 2021Generalization in quantum machine learning from few training datacited by 216
  6. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 195
  7. 2018Quantum generative adversarial networkscited by 128
  8. 2021Federated Quantum Machine Learningcited by 82
  9. 2018Towards Quantum Machine Learning with Tensor Networkscited by 76
  10. 2020Federated Learning Meets Multi-objective Optimizationcited by 53
  11. 2021Quantum Federated Learning with Quantum Datacited by 53
  12. 2021Quantum federated learning through blind quantum computingcited by 44
  13. 2020Approaches and Applications of Early Classification of Time Series: A Reviewcited by 40
  14. 2022Quantum Differential Privacy: An Information Theory Perspectivecited by 32
  15. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  16. 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
  17. 2023Towards Quantum Federated Learningcited by 20
  18. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  19. 2023Federated Quantum Machine Learning with Differential Privacycited by 17
  20. 2023Privacy-preserving quantum federated learning via gradient hidingcited by 15
  21. 2023Federated Quantum Long Short-term Memory (FedQLSTM)cited by 15
  22. 2022Federated Quantum Natural Gradient Descent for Quantum Federated Learningcited by 14
  23. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  24. 2019Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problemscited by 11
  25. 2021Hybrid quantum-classical machine learning for generative chemistry and drug designcited by 10
  26. 2023Quantum Federated Learning With Quantum Networkscited by 8
  27. 2024Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving MLcited by 8
  28. 2024Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcarecited by 7
  29. 2020Compiler Design for Distributed Quantum Computingcited by 6
  30. 2023Quantum Federated Learning: Analysis, Design and Implementation Challengescited by 6
  31. 2025QFAL: Quantum Federated Adversarial Learningcited by 6
  32. 2024A Hybrid Quantum Neural Network for Split Learningcited by 5
  33. 2025Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classificationcited by 5
  34. 2020Accelerating Quantum Approximate Optimization Algorithm using Machine Learningcited by 4
  35. 2023A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discoverycited by 4
  36. 2024Bridging Quantum Computing and Differential Privacy: Insights into Quantum Computing Privacycited by 4
  37. 2024From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networkscited by 4
  38. 2018The Impact of Quantum Computing on Present Cryptographycited by 3

50 most cited of 56 All 56 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  2. 2025Communication Efficient Adaptive Model-Driven Quantum Federated Learningcited by 1
  3. 2025Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applicationscited by 0
  4. 2026Can Quantum Federated Learning Withstand Circuit-Level Backdoors?cited by 0
  5. 2026Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approachcited by 0