There are challenges in training local models at resource-constrained edges in federated learning systems. The limitations in computing, storage, energy consumption, and other aspects constantly affect the scale and effectiveness of the model. Traditional federated pruning methods prune the model during the federated training process, but they fail to prune models adaptively according to the environment and may remove some important parameters, resulting in poor performance of models. This study proposes a distributed model pruning method based on federated reinforcement learning to solve this problem. Firstly, the model pruning process is abstracted, and a Markov decision process is established. DQN algorithm is used to construct a universal reinforcement pruning model, so as to dynamically adjust the pruning rate and improve model generalization performance. Secondly, an aggregation method for sparse models is designed to reinforce and generalize pruning methods, optimize the structure of the model, and reduce its complexity. Finally, this method is compared with different baselines on multiple publicly available datasets. The experimental results show that the proposed method maintains model effectiveness while reducing model complexity.