# QuantumFed: A Federated Learning Framework for Collaborative Quantum Training

arXiv: https://arxiv.org/abs/2106.09109 · HTML: https://mlsanity.com/paper/2106.09109/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)

## Built on by (most cited first, 22 of 22)

- [Towards Quantum Federated Learning](https://mlsanity.com/paper/2306.09912/) (2023)
- [FedQNN: Federated Learning using Quantum Neural Networks](https://mlsanity.com/paper/2403.10861/) (2024)
- [Non-IID Quantum Federated Learning with One-shot Communication Complexity](https://mlsanity.com/paper/2209.00768/) (2022)
- [Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications](https://mlsanity.com/paper/2307.00908/) (2023)
- [Quantum Federated Learning With Quantum Networks](https://mlsanity.com/paper/2310.15084/) (2023)
- [Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare](https://mlsanity.com/paper/2405.07735/) (2024)
- [Scalable Quantum Neural Networks for Classification](https://mlsanity.com/paper/2208.07719/) (2022)
- [Quantum Federated Learning: Analysis, Design and Implementation Challenges](https://mlsanity.com/paper/2306.15708/) (2023)
- [Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation](https://mlsanity.com/paper/2306.11297/) (2023)
- [Experimentally validated quantum-secure federated learning over a multi-user quantum network](https://mlsanity.com/paper/2501.12709/) (2025)
- [Adversarial Robustness of Partitioned Quantum Classifiers](https://mlsanity.com/paper/2502.20403/) (2025)
- [Adversarial Robustness in Distributed Quantum Machine Learning](https://mlsanity.com/paper/2508.11848/) (2025)
- [Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey](https://mlsanity.com/paper/2509.21389/) (2025)
- [Communication Efficient Adaptive Model-Driven Quantum Federated Learning](https://mlsanity.com/paper/2506.04548/) (2025)
- [New Insights on Unfolding and Fine-tuning Quantum Federated Learning](https://mlsanity.com/paper/2506.20016/) (2025)
- [Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework](https://mlsanity.com/paper/2304.13413/) (2023)
- [LLM-QFL: Distilling Large Language Model for Quantum Federated Learning](https://mlsanity.com/paper/2505.18656/) (2025)
- [QFed: Parameter-Compact Quantum-Classical Federated Learning](https://mlsanity.com/paper/2601.09809/) (2026)
- [Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction](https://mlsanity.com/paper/2605.30075/) (2026)
- [A Drift Stable Quantum Federated Learning for Intelligent Services](https://mlsanity.com/paper/2607.21647/) (2026)
- [PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneity](https://mlsanity.com/paper/2608.14995/) (2026)
- [A Stable Aggregation Method for Quantum Federated Learning](https://mlsanity.com/paper/2609.00356/) (2026)

## Abstract (arXiv)

With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machines wish to train a global model using the local data on each machine, it may be very difficult to copy the data into one machine and train the model. Therefore, a collaborative quantum neural network framework is necessary. In this article, we borrow the core idea of federated learning to propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together. Our experiments show the feasibility and robustness of our framework.