mlsanityData to 14 Oct 2026

Quantum Federated Learning: A Comprehensive Survey

arXiv 2508.15998 ↗First posted 21 Aug 2025cs.LGcited by 10

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Abstract (arXiv)

Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving decentralized learning with quantum-enhanced capabilities. It appears as a promising approach for addressing challenges in efficient and secure model training across distributed quantum systems. This paper presents a comprehensive survey on QFL, exploring its key concepts, fundamentals, applications, and emerging challenges in this rapidly developing field. Specifically, we begin with an introduction to the recent advancements of QFL, followed by discussion on its market opportunity and background knowledge. We then discuss the motivation behind the integration of quantum computing and federated learning, highlighting its working principle. Moreover, we review the fundamentals of QFL and its taxonomy. Particularly, we explore federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms within QFL frameworks. Furthermore, we investigate applications of QFL across several domains which include vehicular networks, healthcare networks, satellite networks, metaverse, and network security. Additionally, we analyze frameworks and platforms related to QFL, delving into its prototype implementations, and provide a detailed case study. Key insights and lessons learned from this review of QFL are also highlighted. We complete the survey by identifying current challenges and outlining potential avenues for future research in this rapidly advancing field.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 55 of its 196 references that are in the map. First posted on arXiv 21 Aug 2025.

Builds on

55 of 196 references.

References · 55

  1. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  2. 2020The power of quantum neural networkscited by 379
  3. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  4. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  5. 2020PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentationcited by 175
  6. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  7. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  8. 2022Quantum Differential Privacy: An Information Theory Perspectivecited by 32
  9. 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Aheadcited by 29
  10. 2022QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruningcited by 23
  11. 2022Slimmable Quantum Federated Learningcited by 23
  12. 2019TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learningcited by 22
38 more
  1. 2022Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generationcited by 22
  2. 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
  3. 2023Towards Quantum Federated Learningcited by 20
  4. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  5. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  6. 2018Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Trainingcited by 17
  7. 2023Hybrid quantum image classification and federated learning for hepatic steatosis diagnosiscited by 15
  8. 2018Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier featurescited by 14
  9. 2021Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computerscited by 14
  10. 2022Federated Quantum Natural Gradient Descent for Quantum Federated Learningcited by 14
  11. 2023Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descentcited by 14
  12. 2020Towards a Distributed Quantum Computing Ecosystemcited by 11
  13. 2024IBM Quantum Computers: Evolution, Performance, and Future Directionscited by 11
  14. 2022Quantum neural network autoencoder and classifier applied to an industrial case studycited by 8
  15. 2022Distributed Quantum Computing: a Surveycited by 8
  16. 2025Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approachcited by 8
  17. 2025Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noisecited by 8
  18. 2022Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learningcited by 7
  19. 2021Elsa: Energy-based learning for semi-supervised anomaly detectioncited by 6
  20. 2025QFAL: Quantum Federated Adversarial Learningcited by 6
  21. 2021OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Frameworkcited by 5
  22. 2022Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internetcited by 5
  23. 2023Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementationcited by 5
  24. 2024Safeguarding Next Generation Multiple Access Using Physical Layer Security Techniques: A Tutorialcited by 5
  25. 2024NAC-QFL: Noise Aware Clustered Quantum Federated Learningcited by 5
  26. 2024False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learningcited by 5
  27. 2025Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classificationcited by 5
  28. 2023NISQ Computers: A Path to Quantum Supremacycited by 4
  29. 2024Bridging Quantum Computing and Differential Privacy: Insights into Quantum Computing Privacycited by 4
  30. 2024From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networkscited by 4
  31. 2021Quantum Internet- Applications, Functionalities, Enabling Technologies, Challenges, and Research Directionscited by 3
  32. 2021Management of Resource at the Network Edge for Federated Learningcited by 2
  33. 2025SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualizationcited by 2
  34. 2021Efficient Quantum Network Communication using Optimized Entanglement-Swapping Treescited by 1
  35. 2022Private Set Intersection with Delegated Blind Quantum Computingcited by 1
  36. 2022Privacy and correctness trade-offs for information-theoretically secure quantum homomorphic encryptioncited by 1
  37. 2024Reconfigurable Intelligent Surface (RIS)-Assisted Entanglement Distribution in FSO Quantum Networkscited by 1
  38. 2024Quantum Communication: From Fundamentals to Recent Trends, Challenges and Open Problemscited by 1

50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 7.

10 papers.

  1. 2025Towards Personalized Quantum Federated Learning for Anomaly Detectioncited by 4
  2. 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
  3. 2025Quantum Federated Learning: Architectural Elements and Future Directionscited by 2
  4. 2026Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approachcited by 0
  5. 2026Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methodscited by 0
  6. 2026Quantization in Federated Learning: Methods, Challenges and Future Directionscited by 0
  7. 2026A Drift Stable Quantum Federated Learning for Intelligent Servicescited by 0
  8. 2026Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clientscited by 0
  9. 2026Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approachcited by 0
  10. 2026Efficient Secure Federated Learning via Information-Theoretically Secure Key Distribution: A Medical Imaging Case Studycited by 0