Federated learning enables distributed learning by allowing data to remain on local devices and aggregating the model updates, thereby reducing the risk of data leakage and enhancing privacy protection. However, there are several challenges in federated learning. If participants do not receive appropriate rewards, they may lack the incentive to participate in federated learning tasks. Additionally, the federated learning system may be vulnerable to Byzantine client attackers who intentionally disrupt the model training process and compromise overall performance. Traditional federated learning assumes independent and identically distributed (IID) data, which fails to accommodate data heterogeneity effectively. Therefore, there is an urgent need for an adaptive incentive mechanism explicitly designed for Non-IID data environments. In this paper, we propose FRIFL, a federated learning incentive mechanism that rewards participants based on their reputation and contributions. Considering that each participant may possess data with different distributions and characteristics, we design an attack detection module and account for data heterogeneity. Experimental results demonstrate that FRIFL ensures robustness in unreliable environments with both IID and Non-IID data distributions. Besides, it provides security for the FL system to ensure incentive fairness.