Towards WebAssembly-Based Federated Learning

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
Publisher
Springer Nature Switzerland
ISBN
978-3-031-84617-5
978-3-031-84616-8
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 usingWebAssembly. 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 | WebAssembly | Machine Learning
DDC Class
600: Technology