mlsanityData to 7 Oct 2026

QuantumFed: A Federated Learning Framework for Collaborative Quantum Training

arXiv 2106.09109 ↗First posted 16 Jun 2021cs.LG cs.DCcited by 22

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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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 16 Jun 2021.

Builds on

8 of 25 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2016Enhanced LSTM for Natural Language Inferencecited by 199
  8. 2019Efficient Learning for Deep Quantum Neural Networkscited by 160

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 2; 2025: 7; 2026: 5.

22 papers.

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  1. 2023Towards Quantum Federated Learningcited by 20
  2. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  3. 2022Non-IID Quantum Federated Learning with One-shot Communication Complexitycited by 15
  4. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  5. 2023Quantum Federated Learning With Quantum Networkscited by 8
  6. 2024Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcarecited by 7
  7. 2022Scalable Quantum Neural Networks for Classificationcited by 6
  8. 2023Quantum Federated Learning: Analysis, Design and Implementation Challengescited by 6
  9. 2023Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementationcited by 5
  10. 2025Experimentally validated quantum-secure federated learning over a multi-user quantum networkcited by 5
  11. 2025Adversarial Robustness of Partitioned Quantum Classifierscited by 2
  12. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
10 more
  1. 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
  2. 2025Communication Efficient Adaptive Model-Driven Quantum Federated Learningcited by 1
  3. 2025New Insights on Unfolding and Fine-tuning Quantum Federated Learningcited by 1
  4. 2023Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Frameworkcited by 0
  5. 2025LLM-QFL: Distilling Large Language Model for Quantum Federated Learningcited by 0
  6. 2026QFed: Parameter-Compact Quantum-Classical Federated Learningcited by 0
  7. 2026Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correctioncited by 0
  8. 2026A Drift Stable Quantum Federated Learning for Intelligent Servicescited by 0
  9. 2026PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneitycited by 0
  10. 2026A Stable Aggregation Method for Quantum Federated Learningcited by 0