mlsanityData to 7 Oct 2026

Slimmable Quantum Federated Learning

arXiv 2207.10221 ↗First posted 20 Jul 2022cs.LG quant-phcited by 23

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Abstract (arXiv)

Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to the existing static QFL methods, we propose slimmable QFL (SlimQFL) in this article, which is a dynamic QFL framework that can cope with time-varying communication channels and computing energy limitations. This is made viable by leveraging the unique nature of a QNN where its angle parameters and pole parameters can be separately trained and dynamically exploited. Simulation results corroborate that SlimQFL achieves higher classification accuracy than Vanilla QFL, particularly under poor channel conditions on average.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 20 Jul 2022.

Builds on

13 of 22 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2018Supervised learning with quantum enhanced feature spacescited by 654
  4. 2020Federated Learning with Matched Averagingcited by 564
  5. 2020A Survey on the Explainability of Supervised Machine Learningcited by 225
  6. 2021Supervised quantum machine learning models are kernel methodscited by 199
  7. 2021Federated Quantum Machine Learningcited by 82
  8. 2018Continuous-variable quantum neural networkscited by 74
  9. 2021Quantum Federated Learning with Quantum Datacited by 53
  10. 2021Quantum federated learning through blind quantum computingcited by 44
  11. 2022BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computingcited by 28
  12. 2021Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networkscited by 14
1 more
  1. 2022SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networkscited by 6

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Cited by year

Citing papers per year: 2022: 5; 2023: 8; 2024: 4; 2025: 6.

23 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. 2022Non-IID Quantum Federated Learning with One-shot Communication Complexitycited by 15
  5. 2023Federated Quantum Long Short-term Memory (FedQLSTM)cited by 15
  6. 2025Learning to Measure Quantum Neural Networkscited by 15
  7. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  8. 2023Quantum Federated Learning With Quantum Networkscited by 8
  9. 2025Quantum Federated Learning: A Comprehensive Surveycited by 7
  10. 2023Quantum Federated Learning: Analysis, Design and Implementation Challengescited by 6
  11. 2022Scalable Quantum Convolutional Neural Networkscited by 5
  12. 2023Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementationcited by 5
11 more
  1. 2022Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classificationcited by 4
  2. 2022Quantum Split Neural Network Learning using Cross-Channel Poolingcited by 4
  3. 2022Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Codingcited by 4
  4. 2024From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networkscited by 4
  5. 2023Model-Driven Quantum Federated Learning (QFL)cited by 3
  6. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  7. 2025New Insights on Unfolding and Fine-tuning Quantum Federated Learningcited by 1
  8. 2025Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Seriescited by 1
  9. 2023Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Frameworkcited by 0
  10. 2024Federated learning with distributed fixed design quantum chips and quantum channelscited by 0
  11. 2025LLM-QFL: Distilling Large Language Model for Quantum Federated Learningcited by 0