摘要：The federated learning network requires all the connection weights to be shared among the server and clients during training which increases the risk of data leakage. Meanwhile, the traditional federated learning method has a poor diagnostic effect for non-independently identically distributed data. In order to address these issues, a multi-level federated network based on interpretable indicators was proposed in this manuscript. Firstly, an interpretable adaptive sparse deep network is constructed based on the interpretability principle. Secondly, the relevance map of the network is constructed based on interpretable indicators. Based on this map, the contribution of the connection weights in the network is used to build a multi-level federated network. Finally, the effectiveness of the proposed algorithm has been proved through experimental validation in the paper.
