Publication
Title
Model-driven Quantum Federated Learning (QFL)
Author
Abstract
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.
Language
English
Source (book)
'23 : Companion Proceedings of the 7th International Conference on the Art, Science, and Engineering of Programming, Tokyo, Japan, March 13-17, 2023
Publication
New York, N.Y. : Association for Computing Machinery , 2023
ISBN
979-84-00-70755-1
DOI
10.1145/3594671.3594690
Volume/pages
p. 111-113
ISI
001142109200020
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 20.10.2023
Last edited 11.04.2024
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