Federated learning obtains a shared global model through frequent local training parameter interaction on each participated device. However, the limited communication bandwidth of participated IoT and edge devices will impact communication and learning efficiency. In this paper, a communication efficiency enhanced federated learning technique is presented by proposing a cooperative filter selection method. The Geometric Median of each layer in the global model is adopted as the criterion to cooperatively select important filters in the local model, and then the corresponding parameters interact with other nodes to achieve efficient communication. Multi-scenario experiments verify the communication improvements of our method.