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Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework

arXiv 2304.13413 ↗First posted 26 Apr 2023cs.CR cs.LGcited by 0

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Abstract (arXiv)

We design a model of Post Quantum Cryptography (PQC) Quantum Federated Learning (QFL). We develop a framework with a dynamic server selection and study convergence and security conditions. The implementation and results are publicly available1.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 26 Apr 2023.

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9 of 21 references.

References · 9

  1. 2020The power of quantum neural networkscited by 377
  2. 2020Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognitioncited by 56
  3. 2021Quantum Federated Learning with Quantum Datacited by 53
  4. 2022Slimmable Quantum Federated Learningcited by 23
  5. 2021QuantumFed: A Federated Learning Framework for Collaborative Quantum Trainingcited by 22
  6. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  7. 2022Federated Quantum Natural Gradient Descent for Quantum Federated Learningcited by 14
  8. 2022Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learningcited by 6
  9. 2023Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computingcited by 4

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