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Towards WebAssembly-based federated learning
Publikationstyp
Conference Paper
Date Issued
2025-02
Sprache
English
Author(s)
Gottschalk, Felix
Ebrahimi, Elmira
Kaaser, Dominik
First published in
Number in series
15547 LNCS
Start Page
40
End Page
54
Citation
11th IFIP WG 6.12 European Conference, ESOCC 2025
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Springer
ISBN of container
978-3-031-84617-5
978-3-031-84616-8
978-3-031-84618-2
WebAssembly is a portable binary instruction format designed to serve as a compilation target for high-level languages. While originally developed to run performance-intensive applications directly in Web browsers, WebAssembly supports these days a number of different hardware platforms across the compute continuum. This makes it a promising option to run services for training and inference in Federated Learning. To the best of our knowledge, there have been only a few practical approaches to realize Federated Learning using WebAssembly. Therefore, in this paper, we present a framework to achieve this. Our prototypical implementation shows that WebAssembly-based Federated Learning applications are highly portable while providing acceptable runtime overhead during model training.
Subjects
Federated Learning
Machine Learning
WebAssembly
DDC Class
600: Technology