In the Industrial Internet of Things (IIoT), cross-silo federated learning (CSFL) enables entities, such as manufacturers and suppliers to train global models for optimizing production processes while ensuring data privacy. A well-designed incentive mechanism is essential to persuade clients to contribute data resources. However, existing methodologies overlook the dynamic nature of the training process, where the accuracy of the globally trained model and the client’s data ownership change over time. Furthermore, the majority of previous research assumes a defined functional relationship between the data contribution and the model accuracy, which is infeasible in realistic and dynamic training environments. To address these challenges, we design a novel adaptive mechanism for CSFL that inspires organizations to contribute data resources in a dynamic training environment with the aim of maximizing their long-term payoffs. This mechanism leverages multiagent reinforcement learning (MARL) to ascertain near-optimal data contribution strategies from potential game histories without necessitating private organizational information or a precise accuracy function. Experimental results indicate that our mechanism achieves adaptive incentive in dynamic environments and effectively enhances the long-term payoffs of organizations.