Proof of Learning Applied to Binary Neural Networks

Zoltán-Valentin Gyulai-Nagy

2025

Abstract

This paper introduces a novel method that leverages binary neural networks (BNNs) for transaction validation on blockchains. Utilizing the computational capabilities of traditional Proof-of-Work systems, this approach generates multiple models suitable for real-world applications. BNNs are chosen for their smaller memory footprint, fitting well into blockchain validations and embedding within blocks. The method aligns with the Proof of Learning concept, requiring neural network training to create new blocks, while also incorporating computationally intensive heuristic approaches. Despite the lower precision of BNNs compared to traditional models, their reduced computational demand during inference is beneficial. The goal is to improve their precision through multiple training rounds and the use of evolutionary algorithms. This scalable approach can be customized to meet diverse application needs by allowing users to upload datasets for training specific models. Additionally, it is cost-effective as BNNs can be trained on low-cost devices, broadening access. This strategy aims to refine blockchain validation processes and produce usable models as a byproduct.

Paper Citation

in Harvard Style

Gyulai-Nagy Z. (2025). Proof of Learning Applied to Binary Neural Networks. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 411-418. DOI: 10.5220/0013145800003890

in Bibtex Style

@conference{icaart25,
author={Zoltán-Valentin Gyulai-Nagy},
title={Proof of Learning Applied to Binary Neural Networks},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={411-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013145800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Proof of Learning Applied to Binary Neural Networks
SN - 978-989-758-737-5
AU - Gyulai-Nagy Z.
PY - 2025
SP - 411
EP - 418
DO - 10.5220/0013145800003890
PB - SciTePress