Coin.AI：一个基于区块链的分布式深度学习的工作量证明方案
Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning
摘要：
十年前，比特币被引入，成为第一种加密货币，并建立了“区块链”作为分布式账本的概念。到目前为止，有许多不同的加密货币实现在区块链上工作，具有不同的方法和哲学。然而，它们中的许多都有一个共同的功能：它们需要工作量证明来支持区块的生成（采矿），并最终生成货币。这种工作量证明方案通常包括解决密码学问题，最常见的是破坏哈希值，这只能通过蛮力来实现。工作量证明的主要缺点是，它需要大量的能量，除了支持货币之外，没有任何有用的结果。在本文中，我们提出了一个理论建议，引入了一个有效工作证明方案来支持在区块链上运行的加密货币，我们将其命名为Coin.AI。在该系统中，挖掘方案需要训练深度学习模型，并且仅当这种模型的性能超过阈值时才挖掘块。分布式系统允许节点以简单的方式验证矿工交付的模型（当然比挖掘过程本身更有效），确定何时生成块。此外，本文还提出了一个存储证明方案，用于奖励为深度学习模型提供存储的用户，以及如何将系统的机制与民主化访问人工智能的最终目标相结合的理论论文。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
