# Model-Driven Quantum Federated Learning (QFL)

arXiv: https://arxiv.org/abs/2304.08496 · HTML: https://mlsanity.com/paper/2304.08496/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [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)
- [Slimmable Quantum Federated Learning](https://mlsanity.com/paper/2207.10221/) (2022)
- [GreyCat: Efficient What-If Analytics for Data in Motion at Scale](https://mlsanity.com/paper/1803.09627/) (2018)
- [A Model-Driven Approach to Machine Learning and Software Modeling for the IoT](https://mlsanity.com/paper/2107.02689/) (2021)
- [Towards a Quantum Software Modeling Language](https://mlsanity.com/paper/2006.16690/) (2020)

## Built on by (most cited first, 3 of 3)

- [New Insights on Unfolding and Fine-tuning Quantum Federated Learning](https://mlsanity.com/paper/2506.20016/) (2025)
- [Model-Driven Quantum Code Generation Using Large Language Models and Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.21097/) (2025)
- [Noise-Resilient Quantum Aggregation on NISQ for Federated ADAS Learning](https://mlsanity.com/paper/2512.13196/) (2025)

## Abstract (arXiv)

Recently, several studies have proposed frameworks for Quantum Federated Learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and TensorFlow Federated (TFF) libraries have been deployed for realizing QFL. However, developers, in the main, are not as yet familiar with Quantum Computing (QC) libraries and frameworks. A Domain-Specific Modeling Language (DSML) that provides an abstraction layer over the underlying QC and Federated Learning (FL) libraries would be beneficial. This could enable practitioners to carry out software development and data science tasks efficiently while deploying the state of the art in Quantum Machine Learning (QML). In this position paper, we propose extending existing domain-specific Model-Driven Engineering (MDE) tools for Machine Learning (ML) enabled systems, such as MontiAnna, ML-Quadrat, and GreyCat, to support QFL.