In a blockchain-based federated learning (FL) framework, clients can contribute private data or computing resources to the overall FL training or mining task. To overcome the impractical assumption that participants will voluntarily join training or mining, it is crucial to design an incentive mechanism that motivates participants to achieve optimal training and mining outcomes. In this paper, we investigate the incentive mechanism design for a semi-asynchronous blockchain-based FL system. We model the resource pricing mechanism among clients and task publishers as a Stackelberg game, and prove the existence and uniqueness of a Nash equilibrium in such a game. We then propose an iterative algorithm based on the Alternating Direction Method of Multipliers (ADMM) to achieve the optimal strategies for each participant. Finally, our simulation results verify the convergence and efficiency of our proposed scheme.