# FedQNN: Federated Learning using Quantum Neural Networks

arXiv: https://arxiv.org/abs/2403.10861 · HTML: https://mlsanity.com/paper/2403.10861/

## 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 (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Quantum Federated Learning with Quantum Data](https://mlsanity.com/paper/2106.00005/) (2021)
- [Quantum federated learning through blind quantum computing](https://mlsanity.com/paper/2103.08403/) (2021)
- [Financial Fraud Detection using Quantum Graph Neural Networks](https://mlsanity.com/paper/2309.01127/) (2023)
- [A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead](https://mlsanity.com/paper/2310.10315/) (2023)
- [Slimmable Quantum Federated Learning](https://mlsanity.com/paper/2207.10221/) (2022)
- [QuantumFed: A Federated Learning Framework for Collaborative Quantum Training](https://mlsanity.com/paper/2106.09109/) (2021)
- [Federated Learning with Quantum Secure Aggregation](https://mlsanity.com/paper/2207.07444/) (2022)
- [Foundations of Quantum Federated Learning Over Classical and Quantum Networks](https://mlsanity.com/paper/2310.14516/) (2023)
- [Towards Quantum Federated Learning](https://mlsanity.com/paper/2306.09912/) (2023)
- [Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models](https://mlsanity.com/paper/2308.05237/) (2023)
- [Federated Quantum Machine Learning with Differential Privacy](https://mlsanity.com/paper/2310.06973/) (2023)
- [Non-IID Quantum Federated Learning with One-shot Communication Complexity](https://mlsanity.com/paper/2209.00768/) (2022)
- [Quantum State Tomography using Quantum Machine Learning](https://mlsanity.com/paper/2308.10327/) (2023)
- [Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2401.15804/) (2024)
- [Classical-to-Quantum Sequence Encoding in Genomics](https://mlsanity.com/paper/2304.10786/) (2023)

## Built on by (most cited first, 20 of 20)

- [QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2404.02595/) (2024)
- [Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection](https://mlsanity.com/paper/2412.19441/) (2024)
- [Quantum Federated Learning: A Comprehensive Survey](https://mlsanity.com/paper/2508.15998/) (2025)
- [LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks](https://mlsanity.com/paper/2412.03158/) (2024)
- [A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG](https://mlsanity.com/paper/2503.02497/) (2025)
- [Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML](https://mlsanity.com/paper/2409.11430/) (2024)
- [Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach](https://mlsanity.com/paper/2507.08217/) (2025)
- [MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption](https://mlsanity.com/paper/2412.01858/) (2024)
- [A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection](https://mlsanity.com/paper/2507.22908/) (2025)
- [QFAL: Quantum Federated Adversarial Learning](https://mlsanity.com/paper/2502.21171/) (2025)
- [When Federated Learning Meets Quantum Computing: Survey and Research Opportunities](https://mlsanity.com/paper/2504.08814/) (2025)
- [Towards Personalized Quantum Federated Learning for Anomaly Detection](https://mlsanity.com/paper/2511.07471/) (2025)
- [Adversarial Robustness in Distributed Quantum Machine Learning](https://mlsanity.com/paper/2508.11848/) (2025)
- [SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization](https://mlsanity.com/paper/2508.12477/) (2025)
- [RobQFL: Robust Quantum Federated Learning in Adversarial Environment](https://mlsanity.com/paper/2509.04914/) (2025)
- [Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey](https://mlsanity.com/paper/2509.21389/) (2025)
- [AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing](https://mlsanity.com/paper/2507.07316/) (2025)
- [Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics](https://mlsanity.com/paper/2604.15775/) (2026)
- [Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks](https://mlsanity.com/paper/2605.23324/) (2026)
- [Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach](https://mlsanity.com/paper/2609.25082/) (2026)

## 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.