Federated learning, unlike centralized training, enables multiple clients to train a global model without exposing raw data, making it useful for applications such as medical diagnosis in smart healthcare and mobile edge networks. This setting is vulnerable to dynamic collusive attacks, where colluding clients coordinate abnormal parameter updates and adjust their strategies across training rounds. Existing studies address this problem with isolated mechanisms that focus on a single defense stage, but such mechanisms are insufficient for attacks that evolve across rounds. To address this challenge, we propose reputation-aware Stackelberg incentive aggregation (RSIA) to mitigate the influence of dynamic collusive attacks while balancing model utility in federated learning. Specifically, we first use incentive rules before local training to make repeated collusive behavior less beneficial. We then update client reputation after each round, so earlier unreliable behavior affects later incentives. Finally, we use reputation and update reliability to weight client updates, reducing the influence of abnormal updates on global model training. Experiments on three datasets show that RSIA reduces the average effective attack success rate by 84.9% compared with representative state-of-the-art (SOTA) methods. RSIA also maintains competitive test accuracy, providing a favorable trade-off between model utility and attack suppression.