# Quantum Federated Learning Experiments in the Cloud with Data Encoding

arXiv: https://arxiv.org/abs/2405.00909 · HTML: https://mlsanity.com/paper/2405.00909/

## 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 (most cited first, 3 of 3)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Quantum Federated Learning with Quantum Data](https://mlsanity.com/paper/2106.00005/) (2021)
- [Quantum Computing: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2010.15559/) (2020)

## Built on by (most cited first, 7 of 7)

- [Modeling Quantum Machine Learning for Genomic Data Analysis](https://mlsanity.com/paper/2501.08193/) (2025)
- [SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization](https://mlsanity.com/paper/2508.12477/) (2025)
- [An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data](https://mlsanity.com/paper/2405.09781/) (2024)
- [Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework](https://mlsanity.com/paper/2406.17172/) (2024)
- [New Insights on Unfolding and Fine-tuning Quantum Federated Learning](https://mlsanity.com/paper/2506.20016/) (2025)
- [LLM-QFL: Distilling Large Language Model for Quantum Federated Learning](https://mlsanity.com/paper/2505.18656/) (2025)
- [A Drift Stable Quantum Federated Learning for Intelligent Services](https://mlsanity.com/paper/2607.21647/) (2026)

## 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.