mlsanityData to 7 Oct 2026

Non-IID Quantum Federated Learning with One-shot Communication Complexity

arXiv 2209.00768 ↗First posted 2 Sep 2022quant-ph cs.DC cs.LGcited by 15

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Abstract (arXiv)

Federated learning refers to the task of machine learning based on decentralized data from multiple clients with secured data privacy. Recent studies show that quantum algorithms can be exploited to boost its performance. However, when the clients' data are not independent and identically distributed (IID), the performance of conventional federated algorithms is known to deteriorate. In this work, we explore the non-IID issue in quantum federated learning with both theoretical and numerical analysis. We further prove that a global quantum channel can be exactly decomposed into local channels trained by each client with the help of local density estimators. This observation leads to a general framework for quantum federated learning on non-IID data with one-shot communication complexity. Numerical simulations show that the proposed algorithm outperforms the conventional ones significantly under non-IID settings.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 27 of its 71 references that are in the map. First posted on arXiv 2 Sep 2022.

Builds on

27 of 71 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2015Variational Inference with Normalizing Flowscited by 2,161
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2019Deep Leakage from Gradientscited by 1,004
  7. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  8. 2018Supervised learning with quantum enhanced feature spacescited by 654
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  12. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
15 more
  1. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
  2. 2019One-Shot Federated Learningcited by 124
  3. 2020Distilled One-Shot Federated Learningcited by 108
  4. 2021Provably efficient machine learning for quantum many-body problemscited by 91
  5. 2018Differentiable Learning of Quantum Circuit Born Machinecited by 90
  6. 2021Federated Quantum Machine Learningcited by 82
  7. 2021Quantum Federated Learning with Quantum Datacited by 53
  8. 2021Quantum federated learning through blind quantum computingcited by 44
  9. 2021Recent advances for quantum classifierscited by 35
  10. 2019One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Themcited by 23
  11. 2022Slimmable Quantum Federated Learningcited by 23
  12. 2021QuantumFed: A Federated Learning Framework for Collaborative Quantum Trainingcited by 22
  13. 2021Learning with Density Matrices and Random Featurescited by 17
  14. 2020Supervised Learning with Quantum Measurementscited by 7
  15. 2022CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approachcited by 2

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Cited by year (2026 to 7 Oct)

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

15 papers.

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  1. 2023Towards Quantum Federated Learningcited by 20
  2. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  3. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  4. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  5. 2024Quantum delegated and federated learning via quantum homomorphic encryptioncited by 7
  6. 2025Experimentally validated quantum-secure federated learning over a multi-user quantum networkcited by 5
  7. 2023Empirical Sample Complexity of Neural Network Mixed State Reconstructioncited by 4
  8. 2024Entanglement-induced provable and robust quantum learning advantagescited by 4
  9. 2025SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualizationcited by 2
  10. 2025PSI-PFL: Population Stability Index for Client Selection in non-IID Personalized Federated Learningcited by 1
  11. 2025New Insights on Unfolding and Fine-tuning Quantum Federated Learningcited by 1
  12. 2025Federated Independent Component Analysis via Spectral Alignment and Robust Aggregationcited by 0
3 more
  1. 2025Knowledge Distillation for Variational Quantum Convolutional Neural Networks on Heterogeneous Datacited by 0
  2. 2026Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correctioncited by 0
  3. 2026A Drift Stable Quantum Federated Learning for Intelligent Servicescited by 0