mlsanityData to 14 Oct 2026

Quantum Federated Learning Experiments in the Cloud with Data Encoding

arXiv 2405.00909 ↗First posted 1 May 2024cs.LG cs.ET quant-phcited by 7

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Abstract (arXiv)

Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed data-encoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, shows promising results.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 3 of its 13 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

3 of 13 references.

References · 3

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2021Quantum Federated Learning with Quantum Datacited by 53
  3. 2020Quantum Computing: A Taxonomy, Systematic Review and Future Directionscited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2025Modeling Quantum Machine Learning for Genomic Data Analysiscited by 2
  2. 2025SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualizationcited by 2
  3. 2024An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Datacited by 1
  4. 2024Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Frameworkcited by 1
  5. 2025New Insights on Unfolding and Fine-tuning Quantum Federated Learningcited by 1
  6. 2025LLM-QFL: Distilling Large Language Model for Quantum Federated Learningcited by 0
  7. 2026A Drift Stable Quantum Federated Learning for Intelligent Servicescited by 0