ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems

Tran, Nguyen Khoi; Sabir, Bushra; Babar, Muhammad Ali; Cui, Nini; Lipman, Justin

doi:10.1007/978-3-031-16697-6_4

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Coelho et al (2022) proposes a blockchain platform architecture for collaborative research and reproducibility of experiment results, evaluating it using a scenario about genomic sequencing of the SARS-CoV-2 coronavirus. Khoi Tran et al (2022) designs a decentralized platform based on blockchain and smart contracts for disseminating, storing, and updating ML provenance. They propose a novel architectural approach called Artefact-as-a-State-Machine to manage ML provenance on a blockchain.…”

Section: Related Workmentioning

confidence: 99%

Advancing Research Reproducibility in Machine Learning through Blockchain Technology

Filatovas,

Stripinis,

Orts

et al. 2024

Informatica

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Like other disciplines, machine learning is currently facing a reproducibility crisis that hinders the advancement of scientific research. Researchers face difficulties reproducing key results due to the lack of critical details, including the disconnection between publications and associated models, data, parameter settings, and experimental results. To promote transparency and trust in research, solutions that improve the accessibility of models and data, facilitate experiment tracking, and allow audit of experimental results are needed. Blockchain technology, characterized by its decentralization, data immutability, cryptographic hash functions, consensus algorithms, robust security measures, access control mechanisms, and innovative smart contracts, offers a compelling pathway for the development of such solutions. To address the reproducibility challenges in machine learning, we present a novel concept of a blockchain-based platform that operates on a peer-to-peer network. This network comprises organizations and researchers actively engaged in machine learning research, seamlessly integrating various machine learning research and development frameworks. To validate the viability of our proposed concept, we implemented a blockchain network using the Hyperledger Fabric infrastructure and conducted experimental simulations in several scenarios to thoroughly evaluate its effectiveness. By fostering transparency and facilitating collaboration, our proposed platform has the potential to significantly improve reproducible research in machine learning and can be adapted to other domains within artificial intelligence.

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Section: Related Workmentioning

confidence: 99%