Federated learning， as a distributed machine learning framework for data privacy protection， faces several challenges such as that the traditional cloud-edge-device structure cannot meet the increasingly strict data security regulations and the exponential growth of data. At the same time， the lightweight trend of edge devices leads to limited computing capabilities. To address these issues， an Optional Hierarchical Federated Learning （OHFL） scheme facing multi-level supervision and heterogeneous resource environments was proposed. Firstly， a tree-structured multi-layer architecture was adopted to configure the number of edge layers and the number of servers per layer flexibly， so as to adapt to supervision levels and resource distribution in different scenarios. Secondly， a differential privacy mechanism was introduced on the basis of ensuring communication efficiency， homomorphic encryption was applied to certain neural network layers， and the encryption tasks were offloaded to the nearest edge servers to distribute the computational burden. Experimental results show that the OHFL scheme achieves a classification accuracy of 97.82% on the MNIST Independent Identically Distributed （IID） dataset， with the additional encryption mechanism using Convolutional Neural Network （CNN） and ResNet18 incurring only a time overhead of 6.24 s and 6.80 s per round， verifying that the OHFL scheme improves the system efficiency of computation and communication significantly. It can be seen that the proposed scheme improves the security and adaptability of hierarchical federated learning architecture theoretically， and provides a feasible solution for meeting multi-level supervision and efficient secure computation in practical applications， especially suitable for scenarios such as Internet of Things （IoT） and healthcare with data sensitivity and resource heterogeneity.