Model-driven Quantum Federated Learning (QFL)Moin, A., Badii, A. and Challenger, M. (2023) Model-driven Quantum Federated Learning (QFL). In: '23 Companion: Companion Proceedings of the 7th International Conference on the Art, Science, and Engineering of Programming, 13-17 Mar 2023, Tokyo, Japan, pp. 111-113, 10.1145/3594671.3594690.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1145/3594671.3594690 Abstract/SummaryABSTRACT 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.
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