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Title
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Model-driven Quantum Federated Learning (QFL)
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Author
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Abstract
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| 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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Language
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English
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Source (book)
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'23 : Companion Proceedings of the 7th International Conference on the Art, Science, and Engineering of Programming, Tokyo, Japan, March 13-17, 2023
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Publication
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New York, N.Y.
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Association for Computing Machinery
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2023
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ISBN
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979-84-00-70755-1
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DOI
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10.1145/3594671.3594690
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Volume/pages
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p. 111-113
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ISI
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001142109200020
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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