In recent years, privacy threats have been rising in a flood of data. Federated learning was introduced to protect the privacy of data in machine learning. However, Internet of Things (IoT) devices accounting for a large portion of data collection still have weak computational and communication power. Moreover, cutting-edged image classification architectures have more extensive and complex models to reach high performance. In this paper, we introduce FedTH, a tree-based hierarchical image classification architecture in federated learning, to handle these problems. FedTH architecture is constructed of a tree structure to help decrease computational and communication costs, to have a flexible prediction procedure, and to have robustness in heterogeneous environments.