The Industrial Internet of Things (IIoT) leverages Federated Learning (FL) for distributed model training while preserving data privacy, and meta-computing enhances FL by optimizing and integrating distributed computing resources, improving efficiency and scalability. Efficient IIoT operations require a trade-off between model quality and training latency. Consequently, a primary challenge of FL in IIoT is to optimize overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency for meta-computing. Additionally, the satisfaction function is incorporated into the utility function to incentivize IIoT nodes to participate in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for IIoT. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves utility by at least 23.7% compared to existing FL schemes without compromising model accuracy.

翻译：工业物联网（IIoT）利用联邦学习（FL）进行分布式模型训练以保护数据隐私，而元计算通过优化和整合分布式计算资源来增强FL，提升效率和可扩展性。高效的IIoT运行需要在模型质量与训练延迟之间取得平衡。因此，IIoT中FL的一个核心挑战是通过平衡模型质量和训练延迟来优化整体系统性能。本文设计了一种考虑数据规模、信息年龄（AoI）和训练延迟的元计算满意度函数。此外，该满意度函数被纳入效用函数，以激励IIoT节点参与模型训练。我们将服务器和节点的效用函数建模为两阶段Stackelberg博弈，并采用深度强化学习方法学习Stackelberg均衡。该方法确保了奖励的平衡，并增强了IIoT激励方案的适用性。仿真结果表明，在相同预算约束下，与现有FL方案相比，所提出的激励方案在不损害模型精度的前提下，将效用提升了至少23.7%。