mlsanityData to 14 Oct 2026

FedQNN: Federated Learning using Quantum Neural Networks

arXiv 2403.10861 ↗First posted 16 Mar 2024quant-ph cs.ET cs.LGcited by 20

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Abstract (arXiv)

In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about data privacy and the exposure of sensitive information. Our proposed Federated Quantum Neural Network (FedQNN) framework emerges as a cutting-edge solution, integrating the singular characteristics of QML with the principles of classical federated learning. This work thoroughly investigates QFL, underscoring its capability to secure data handling in a distributed environment and facilitate cooperative learning without direct data sharing. Our research corroborates the concept through experiments across varied datasets, including genomics and healthcare, thereby validating the versatility and efficacy of our FedQNN framework. The results consistently exceed 86% accuracy across three distinct datasets, proving its suitability for conducting various QML tasks. Our research not only identifies the limitations of classical paradigms but also presents a novel framework to propel the field of QML into a new era of secure and collaborative innovation.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 16 Mar 2024.

Builds on

18 of 41 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Efficient Learning for Deep Quantum Neural Networkscited by 161
  3. 2021Federated Quantum Machine Learningcited by 82
  4. 2021Quantum Federated Learning with Quantum Datacited by 53
  5. 2021Quantum federated learning through blind quantum computingcited by 44
  6. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  7. 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Aheadcited by 29
  8. 2022Slimmable Quantum Federated Learningcited by 23
  9. 2021QuantumFed: A Federated Learning Framework for Collaborative Quantum Trainingcited by 22
  10. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  11. 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
  12. 2023Towards Quantum Federated Learningcited by 20
6 more
  1. 2023Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Modelscited by 19
  2. 2023Federated Quantum Machine Learning with Differential Privacycited by 17
  3. 2022Non-IID Quantum Federated Learning with One-shot Communication Complexitycited by 15
  4. 2023Quantum State Tomography using Quantum Machine Learningcited by 7
  5. 2024Brain Tumor Diagnosis Using Quantum Convolutional Neural Networkscited by 4
  6. 2023Classical-to-Quantum Sequence Encoding in Genomicscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 12; 2026: 3.

20 papers.

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  1. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  2. 2024Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detectioncited by 10
  3. 2025Quantum Federated Learning: A Comprehensive Surveycited by 10
  4. 2024LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networkscited by 9
  5. 2025A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAGcited by 9
  6. 2024Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving MLcited by 8
  7. 2025Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approachcited by 8
  8. 2024MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryptioncited by 7
  9. 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
  10. 2025QFAL: Quantum Federated Adversarial Learningcited by 6
  11. 2025When Federated Learning Meets Quantum Computing: Survey and Research Opportunitiescited by 5
  12. 2025Towards Personalized Quantum Federated Learning for Anomaly Detectioncited by 4
8 more
  1. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  2. 2025SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualizationcited by 2
  3. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
  4. 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
  5. 2025AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparingcited by 0
  6. 2026Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physicscited by 0
  7. 2026Enhancing Blood Cells Classification using Hybrid Quantum Neural Networkscited by 0
  8. 2026Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approachcited by 0