As artificial intelligence (AI) continues to permeate various domains, concerns surrounding trust and transparency in AI-driven inference and training processes have emerged, particularly with respect to potential biases and traceability challenges. Decentralized solutions such as blockchain have been proposed to tackle these issues, but they often struggle when dealing with large-scale models, leading to time-consuming inference and inefficient training verification. To overcome these limitations, we introduce BRAIN, a Blockchain-based Reliable AI Network, a novel platform specifically designed to ensure reliable inference and training of large models. BRAIN harnesses a unique two-phase transaction mechanism, allowing real-time processing via pipelining by separating request and response transactions. Each randomly-selected inference committee commits and reveals the inference results, and upon reaching an agreement through a smart contract, then the requested operation is executed using the consensus result. Additionally, BRAIN carries out training by employing a randomly-selected training committee. They submit commit and reveal transactions along with their respective scores, enabling local model aggregation based on the median value of the scores. Experimental results demonstrate that BRAIN delivers considerably higher inference throughput at reasonable gas fees. In particular, BRAIN's tasks-per-second performance is 454.4293 times greater than that of a naive single-phase implementation.

翻译：随着人工智能（AI）持续渗透至各个领域，围绕AI驱动推理与训练过程中的信任与透明度问题日益凸显，尤其体现在潜在偏差与可追溯性挑战方面。去中心化方案（如区块链）已被提出以应对这些问题，但在处理大规模模型时往往面临困境，导致推理耗时长、训练验证效率低下。为克服这些限制，我们提出BRAIN（基于区块链的可靠人工智能网络），这是一个专为确保大规模模型可靠推理与训练而设计的新型平台。BRAIN采用独特的两阶段交易机制，通过分离请求与响应交易，借助流水线实现实时处理。每个随机选取的推理委员会提交并揭示推理结果，经智能合约达成共识后，基于共识结果执行所请求的操作。此外，BRAIN通过随机选取的训练委员会执行训练过程：委员们提交存证与揭示交易及其各自评分，从而基于评分中值实现本地模型聚合。实验结果表明，BRAIN在合理Gas费用下可实现显著更高的推理吞吐量。具体而言，BRAIN的每秒任务处理性能是朴素单阶段实现的454.4293倍。