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Towards WebAssembly-Based Federated Learning

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Service-Oriented and Cloud Computing (ESOCC 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15547))

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Abstract

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.

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Notes

  1. 1.

    https://github.com/WebAssembly/WASI.

  2. 2.

    https://github.com/VerFlix/WASM-FL.

  3. 3.

    https://wasmtime.dev/.

  4. 4.

    https://github.com/FederatedAI/FATE.

  5. 5.

    https://github.com/IBM/federated-learning-lib.

  6. 6.

    https://ai.google.dev/edge/litert.

  7. 7.

    https://www.mlpack.org/.

  8. 8.

    https://github.com/minhpqn/maxent.

  9. 9.

    https://pytorch.org/cppdocs/.

  10. 10.

    https://github.com/libfann/fann.

  11. 11.

    https://github.com/WebAssembly/wasi-sockets.

  12. 12.

    https://github.com/WebAssembly/wasi-http.

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Acknowledgement

The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development as well as the Christian Doppler Research Association for the Christian Doppler Laboratory for Blockchain Technologies for the Internet of Things is gratefully acknowledged.

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Authors and Affiliations

  1. Christian Doppler Laboratory for Blockchain Technologies for the Internet of Things, Institute for Data Engineering, TU Hamburg, Hamburg, Germany

    Felix Gottschalk, Stefan Schulte, Nisal Hemadasa, Elmira Ebrahimi & Dominik Kaaser

  2. University of Hamburg, Hamburg, Germany

    Janick Edinger

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  1. Felix Gottschalk
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  2. Stefan Schulte
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  3. Nisal Hemadasa
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  4. Elmira Ebrahimi
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  5. Janick Edinger
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  6. Dominik Kaaser
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Correspondence to Stefan Schulte .

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Editors and Affiliations

  1. Free University of Bozen-Bolzano, Bolzano, Bolzano, Italy

    Claus Pahl

  2. Free University of Bozen-Bolzano, Bolzano, Italy

    Andrea Janes

  3. The University of Arizona, Tucson, AZ, USA

    Tomas Cerny

  4. University of Oulu, Oulu, Finland

    Valentina Lenarduzzi

  5. University of Oulu, Oulu, Finland

    Matteo Esposito

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Gottschalk, F., Schulte, S., Hemadasa, N., Ebrahimi, E., Edinger, J., Kaaser, D. (2025). Towards WebAssembly-Based Federated Learning. In: Pahl, C., Janes, A., Cerny, T., Lenarduzzi, V., Esposito, M. (eds) Service-Oriented and Cloud Computing. ESOCC 2025. Lecture Notes in Computer Science, vol 15547. Springer, Cham. https://doi.org/10.1007/978-3-031-84617-5_4

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