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, and also improves the computational overhead from $Θ(1)$ 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：大多数主流区块链系统高度依赖工作量证明（PoW）或权益证明（PoS）机制来实现去中心化共识与安全保障。然而，传统PoW方法中源于计算密集但无意义任务的巨大能源消耗已引发广泛争议；PoS机制虽无能耗问题，却面临安全与经济性挑战。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任务作为PoW，从而赋予能源消耗以实质价值。尽管此前在学习证明（PoL）领域的探索尝试将深度学习模型训练的SGD任务作为PoUW挑战，但近期研究揭示了其对对抗攻击的脆弱性，以及构建拜占庭安全的PoL机制在理论上的困难。本文提出"激励安全性"概念，通过激励理性证明者出于自身最大利益而诚实行为，规避现有理论难题，设计了一种兼具计算效率、可证明激励安全性保证与难度可控的PoL机制。特别地，本工作能抵御两种攻击，并将计算开销从$Θ(1)$降低至$O(\frac{\log E}{E})$。此外，针对近期多数研究假设问题提供者和验证者可信的情况，本设计即使在问题提供者不可信时仍能保证前端激励安全性，同时通过绕过验证者困境实现验证者激励安全性。通过将机器学习训练以可证明性保障融入区块链共识机制，本研究不仅为区块链系统提出环保解决方案，更在人工智能新时代为完全去中心化的算力市场提供了可行方案。