Large proof of work (PoW) networks allow anyone to earn rewards by running computation-intensive hash puzzles for profit, yet they typically consume electricity comparable to that of medium-sized countries. Repurposing computing resources from hash puzzles to machine learning training can benefit the energy sector as a whole, since this computing power is no longer wasted on solving hash puzzles but is instead used to train machine learning models that provide value across different application domains. However, major technical gaps currently prevent this integration. To bridge these gaps, we introduce proof of training (PoT), a protocol that directs mining power toward verifiable training of machine learning models while preserving PoW's incentives for participation and growth. We study PoT by theoretically identifying the blockchain structure that best meets the goals of training reliability, security, and scalability, and we further evaluate it by implementing a decentralized training network. Our results indicate considerable potential, including high task throughput, strong robustness, and improved network security.

翻译：大型工作量证明（PoW）网络允许任何人通过运行计算密集型的哈希谜题来赚取奖励，但其通常消耗相当于中等国家规模的电力。将哈希谜题的计算资源重新用于机器学习训练可惠及整个能源领域，因为这部分算力不再浪费于破解哈希谜题，而是用于训练能为不同应用领域提供价值的机器学习模型。然而，当前存在重大技术差距阻碍这种整合。为弥合这些差距，我们提出训练证明（PoT）协议，该协议在保持PoW参与激励与网络增长特性的同时，将挖矿算力导向可验证的机器学习模型训练。我们通过理论分析识别最能实现训练可靠性、安全性和可扩展性目标的区块链架构来研究PoT，并通过实现去中心化训练网络对其进行评估。实验结果表明该方法具有显著潜力，包括高任务吞吐量、强鲁棒性以及改进的网络安全性。