Repeated Game-Based Long-Term Incentive Mechanism for Blockchain-Enabled Reliable Federated Learning in IIoT

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative model training in the Industrial Internet of Things (IIoT). By leveraging the decentralization, immutability, and transparency of blockchain technology, Blockchain-enabled FL (BFL) has gained significant attention for enhancing FL’s security and reliability. However, BFL still faces challenges in motivating client participation. While several incentive mechanisms have been proposed, most primarily focus on short-term rewards and overlook the long-term influence of individual contributions on global model performance. To address these challenges, we propose a novel BFL framework that integrates model training with blockchain mining on the client side. Specifically, we design a long-term incentive mechanism based on repeated game theory, where the interactions between participants and the task publisher (TP) are modeled as an infinitely repeated game. We formally prove the existence of a Subgame Perfect Nash Equilibrium, providing theoretical guarantees for stable long-term cooperation. Furthermore, we introduce a hybrid reward scheme that jointly considers contributions to both training and mining tasks, encouraging sustained engagement and attracting new participants. Extensive experiments on MNIST and CIFAR-10 validate that the proposed mechanism enhances the robustness of FL and effectively promotes long-term client participation.