Publication: Coin.AI: A proof-of-useful-work scheme for blockchain-based distributed deep learning
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2019-08
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MDPI
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Abstract
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.
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This article belongs to the Special Issue Blockchain: Security, Challenges, and Opportunities.
Keywords
Blockchain, Cryptocurrency, Neural networks, Deep learning, Proof-of-work
Bibliographic citation
Baldominos, A., & Saez, Y. (2019). Coin.AI: A Proof-of-Useful-Work scheme for Blockchain-Based distributed deep learning. Entropy, 21(8), 723.