Towards WebAssembly-based federated learning

Publikationstyp
Conference Paper
Date Issued
2025-02
Sprache
English
Author(s)
Gottschalk, Felix
Schulte, Stefan  
Data Engineering E-19  
Manikku Badu, Nisal Hemadasa  
Data Engineering E-19  
Ebrahimi, Elmira 
Data Engineering E-19  
Edinger, Janick  
Schallmoser, Dominik 
Data Engineering E-19  
TORE-URI
https://hdl.handle.net/11420/55039
First published in
Lecture notes in computer science  
Number in series
15547 LNCS
Start Page
40
End Page
54
Citation
Lecture notes in computer science 15547 LNCS: 40-54 (2025)
Contribution to Conference
11th IFIP WG 6.12 European Conference, ESOCC 2025  
Publisher DOI
10.1007/978-3-031-84617-5_4
Scopus ID
2-s2.0-86000030535
Publisher
Springer
ISBN
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