Gottschalk, FelixFelixGottschalkSchulte, StefanStefanSchulteManikku Badu, Nisal HemadasaNisal HemadasaManikku BaduEbrahimi, ElmiraElmiraEbrahimiEdinger, JanickJanickEdingerSchallmoser, DominikDominikSchallmoser2025-03-282025-03-282025-02Lecture notes in computer science 15547 LNCS: 40-54 (2025)978-3-031-84617-5978-3-031-84616-8978-3-031-84618-2https://hdl.handle.net/11420/55039WebAssembly 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.enFederated Learning | Machine Learning | WebAssemblyTechnology::600: TechnologyConference Paper10.1007/978-3-031-84617-5_4Conference Paper