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Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning
One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of “blockchain” as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, ma...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515252/ https://www.ncbi.nlm.nih.gov/pubmed/33267437 http://dx.doi.org/10.3390/e21080723 |
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author | Baldominos, Alejandro Saez, Yago |
author_facet | Baldominos, Alejandro Saez, Yago |
author_sort | Baldominos, Alejandro |
collection | PubMed |
description | One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of “blockchain” as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence. |
format | Online Article Text |
id | pubmed-7515252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75152522020-11-09 Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning Baldominos, Alejandro Saez, Yago Entropy (Basel) Article One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of “blockchain” as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence. MDPI 2019-07-25 /pmc/articles/PMC7515252/ /pubmed/33267437 http://dx.doi.org/10.3390/e21080723 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Baldominos, Alejandro Saez, Yago Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning |
title | Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning |
title_full | Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning |
title_fullStr | Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning |
title_full_unstemmed | Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning |
title_short | Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning |
title_sort | coin.ai: a proof-of-useful-work scheme for blockchain-based distributed deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515252/ https://www.ncbi.nlm.nih.gov/pubmed/33267437 http://dx.doi.org/10.3390/e21080723 |
work_keys_str_mv | AT baldominosalejandro coinaiaproofofusefulworkschemeforblockchainbaseddistributeddeeplearning AT saezyago coinaiaproofofusefulworkschemeforblockchainbaseddistributeddeeplearning |