Blockchain has been used in several domains. However, this technology still has major limitations that are largely related to one of its core components, namely the consensus protocol/algorithm. Several solutions have been proposed in literature and some of them are based on the use of Machine Learning (ML) methods. The ML-based consensus algorithms usually waste the work done by the (contributing/participating) nodes, as only winners' ML models are considered/used, resulting in low energy efficiency. To reduce energy waste and improve scalability, this paper proposes an AI-enabled consensus algorithm (named AICons) driven by energy preservation and fairness of rewarding nodes based on their contribution. In particular, the local ML models trained by all nodes are utilised to generate a global ML model for selecting winners, which reduces energy waste. Considering the fairness of the rewards, we innovatively designed a utility function for the Shapley value evaluation equation to evaluate the contribution of each node from three aspects, namely ML model accuracy, energy consumption, and network bandwidth. The three aspects are combined into a single Shapley value to reflect the contribution of each node in a blockchain system. Extensive experiments were carried out to evaluate fairness, scalability, and profitability of the proposed solution. In particular, AICons has an evenly distributed reward-contribution ratio across nodes, handling 38.4 more transactions per second, and allowing nodes to get more profit to support a bigger network than the state-of-the-art schemes.

翻译：区块链已在多个领域得到应用。然而，该技术仍存在重大局限性，这主要与其核心组件之一——共识协议/算法密切相关。已有文献提出多种解决方案，其中一些基于机器学习方法的运用。基于机器学习的共识算法通常会浪费（贡献/参与）节点的工作，因为只有获胜者的机器学习模型被考虑/使用，导致能效低下。为减少能量浪费并提升可扩展性，本文提出一种以能量保存和基于贡献的节点奖励公平性为驱动的AI共识算法（命名为AICons）。具体而言，所有节点训练的本地机器学习模型被用于生成一个全局机器学习模型以选择获胜者，从而减少能量浪费。考虑到奖励的公平性，我们创新性地为夏普利值评估方程设计了一个效用函数，从三个维度评估每个节点的贡献，即机器学习模型准确率、能耗和网络带宽。这三个维度被整合为一个夏普利值，以反映区块链系统中每个节点的贡献。通过大量实验评估了所提方案的公平性、可扩展性和盈利能力。特别地，与现有最优方案相比，AICons在节点间实现均匀分布的奖励-贡献比，每秒处理交易量增加38.4倍，并允许节点获得更高利润以支持更大规模网络。