mlsanityData to 7 Oct 2026

Personalized Quantum Federated Learning for Privacy Image Classification

arXiv 2410.02547 ↗First posted 3 Oct 2024quant-ph cs.AIcited by 4

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Abstract (arXiv)

Quantum federated learning has brought about the improvement of privacy image classification, while the lack of personality of the client model may contribute to the suboptimal of quantum federated learning. A personalized quantum federated learning algorithm for privacy image classification is proposed to enhance the personality of the client model in the case of an imbalanced distribution of images. First, a personalized quantum federated learning model is constructed, in which a personalized layer is set for the client model to maintain the personalized parameters. Second, a personalized quantum federated learning algorithm is introduced to secure the information exchanged between the client and server.Third, the personalized federated learning is applied to image classification on the FashionMNIST dataset, and the experimental results indicate that the personalized quantum federated learning algorithm can obtain global and local models with excellent performance, even in situations where local training samples are imbalanced. The server's accuracy is 100% with 8 clients and a distribution parameter of 100, outperforming the non-personalized model by 7%. The average client accuracy is 2.9% higher than that of the non-personalized model with 2 clients and a distribution parameter of 1. Compared to previous quantum federated learning algorithms, the proposed personalized quantum federated learning algorithm eliminates the need for additional local training while safeguarding both model and data privacy.It may facilitate broader adoption and application of quantum technologies, and pave the way for more secure, scalable, and efficient quantum distribute machine learning solutions.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 45 references that are in the map. First posted on arXiv 3 Oct 2024.

Builds on

18 of 45 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  4. 2021Towards Personalized Federated Learningcited by 462
  5. 2020Power of data in quantum machine learningcited by 336
  6. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  7. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 95
  8. 2021Quantum federated learning through blind quantum computingcited by 44
  9. 2023Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learningcited by 39
  10. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  11. 2022QSAN: A Near-term Achievable Quantum Self-Attention Networkcited by 25
  12. 2023Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedupcited by 23
6 more
  1. 2023QKSAN: A Quantum Kernel Self-Attention Networkcited by 23
  2. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  3. 2024FedPFT: Federated Proxy Fine-Tuning of Foundation Modelscited by 16
  4. 2023Hybrid quantum image classification and federated learning for hepatic steatosis diagnosiscited by 15
  5. 2023Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentificationcited by 7
  6. 2022Federated Learning with Privacy-Preserving Ensemble Attention Distillationcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noisecited by 8
  2. 2025Towards Heterogeneous Quantum Federated Learning: Challenges and Solutionscited by 0
  3. 2026Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approachcited by 0
  4. 2026PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneitycited by 0