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Towards Reproducible Research in Machine Learning via Blockchain

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Numerical Computations: Theory and Algorithms (NUMTA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14478))

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Abstract

Artificial Intelligence, particularly in Machine Learning and related research areas such as Operational Research, currently faces a reproducibility crisis. Researchers encounter difficulties reproducing key results due to lacking critical details, including the disconnection between publications and the associated codes, data, and parameter settings. Solutions that improve code accessibility, data provenance tracking, research transparency, auditing of obtained results, and trust can significantly accelerate algorithm and model development, validation, and transition into real-world applications. Blockchain technology, with its features of decentralization, data immutability, cryptographic hash functions, and consensus algorithms, provides a promising avenue for developing such solutions. By leveraging the distributed ledger working over a peer-to-peer network, a secure and auditable infrastructure can be established for sharing and controlling data in a trusted manner. Our analysis examines the current state-of-the-art blockchain-based proposals that target reproducibility issues in the Machine Learning domain. Based on the analysis of existing solutions, we propose a high-level architecture and main modules for developing a blockchain-based platform that enhances reproducible research in Machine Learning and can be adapted to other Artificial Intelligence domains.

This research has received funding from the Research Council of Lithuania (LMTLT), agreement No. S-MIP-21-53.

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Notes

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  2. 2.

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  5. 5.

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Acknowledgements

This research has received funding from the Research Council of Lithuania (LMTLT), agreement No. S-MIP-21-53.

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Authors and Affiliations

  1. Vilnius University Institute of Data Science and Digital Technologies, Akademijos 4, 08663, Vilnius, Lithuania

    Ernestas Filatovas, Linas Stripinis, Francisco Orts & Remigijus Paulavičius

Authors
  1. Ernestas Filatovas
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  2. Linas Stripinis
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  3. Francisco Orts
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  4. Remigijus Paulavičius
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Correspondence to Ernestas Filatovas .

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Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

  3. University of Calabria, Rende, Italy

    Annabella Astorino

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Filatovas, E., Stripinis, L., Orts, F., Paulavičius, R. (2025). Towards Reproducible Research in Machine Learning via Blockchain. In: Sergeyev, Y.D., Kvasov, D.E., Astorino, A. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2023. Lecture Notes in Computer Science, vol 14478. Springer, Cham. https://doi.org/10.1007/978-3-031-81247-7_24

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