Vol. 3 No. 1 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Blockchain and Federated Learning: Securing Decentralized Machine Learning Systems

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  • Dr. Peter Murphy
Dr. Peter Murphy
Professor of Computer Science, Dublin City University, Ireland
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Published 30-06-2023

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Peter Murphy, “Blockchain and Federated Learning: Securing Decentralized Machine Learning Systems”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 236–247, Jun. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/85

Abstract

Overall, our work continues the nascent exploration of blockchain and distributed ledger technologies for enhancing privacy and security, and argues that it could be fruitfully combined with the fostered decentralization in machine learning. We do not advocate blockchain as the new best mechanism for securing every machine learning update, but we provide a working prototype as a step towards the use of the technology in practice for the first layer of a decentralized machine-learning system.

With this objective, alongside a thorough description of the setting and mechanics of decentralized training using federated learning, we make two main contributions. First, we provide a concrete methodology to apply a permissioned blockchain to secure federated learning in a privacy-friendly and performance-aware fashion – even amid a non-collaborative adversary. Second, we propose to extend the use of blockchain to securely share knowledge between models in a similar privacy-preserving fashion. We implement a first proof of concept of this blockchain-based approach, develop a mechanism to provide strong and efficient security guarantees, and experimentally evaluate its performance.

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