Most concurrent blockchain systems rely heavily on the Proof-of-Work (PoW) or Proof-of-Stake (PoS) mechanisms for decentralized consensus and security assurance. However, the substantial energy expenditure stemming from computationally intensive yet meaningless tasks has raised considerable concerns surrounding traditional PoW approaches, The PoS mechanism, while free of energy consumption, is subject to security and economic issues. Addressing these issues, the paradigm of Proof-of-Useful-Work (PoUW) seeks to employ challenges of practical significance as PoW, thereby imbuing energy consumption with tangible value. While previous efforts in Proof of Learning (PoL) explored the utilization of deep learning model training SGD tasks as PoUW challenges, recent research has revealed its vulnerabilities to adversarial attacks and the theoretical hardness in crafting a byzantine-secure PoL mechanism. In this paper, we introduce the concept of incentive-security that incentivizes rational provers to behave honestly for their best interest, bypassing the existing hardness to design a PoL mechanism with computational efficiency, a provable incentive-security guarantee and controllable difficulty. Particularly, our work is secure against two attacks to the recent work of Jia et al. [2021], and also improves the computational overhead from $\Theta(1)$ to $O(\frac{\log E}{E})$. Furthermore, while most recent research assumes trusted problem providers and verifiers, our design also guarantees frontend incentive-security even when problem providers are untrusted, and verifier incentive-security that bypasses the Verifier's Dilemma. By incorporating ML training into blockchain consensus mechanisms with provable guarantees, our research not only proposes an eco-friendly solution to blockchain systems, but also provides a proposal for a completely decentralized computing power market in the new AI age.

翻译：大多数并行区块链系统严重依赖工作量证明（PoW）或权益证明（PoS）机制来实现去中心化共识和安全保障。然而，传统PoW方法因执行计算密集型却无实际意义的任务而消耗大量能源，引发了广泛关注；PoS机制虽无能源消耗，却存在安全与经济问题。针对这些问题，"有用工作量证明"（PoUW）范式试图采用具有实际意义的挑战作为PoW，从而赋予能源消耗以实际价值。尽管前期学习证明（PoL）研究探索了将深度学习模型训练的SGD任务作为PoUW挑战，但近年研究已揭示其易受对抗攻击，且构建拜占庭安全的PoL机制在理论上存在困难。本文提出"激励安全性"概念，通过激励理性证明者为自身最大利益而诚实行为，绕过现有理论难点，设计出具有计算效率、可证明激励安全性保证和可控难度的PoL机制。具体而言，本工作能够抵御Jia等人[2021]近期研究中的两种攻击，并将计算开销从Θ(1)降低至O((log E)/E)。此外，尽管大多数近期研究假设问题提供者和验证者可信，但本设计在问题提供者不可信时仍能保证前端激励安全性，并实现绕过"验证者困境"的验证者激励安全性。通过将机器学习训练与具有可证明保证的区块链共识机制相结合，本研究不仅为区块链系统提供了环保解决方案，也为人工智能新时代下完全去中心化算力市场提出了可行方案。