A Model Distributed Training Method Based on Improved Split Learning in the Internet of Things

With the rapid development of the Internet of Things (IoT), a large amount of data has been brought to distributed nodes in edge environments, which has caused concerns about data privacy leakage and data transmission latency. Split learning (SL) divides the model among distributed nodes, addressing the high storage and computational demands brought by storing the entire model on a single node in federated learning. However, SL suffers from scalability limitations, leading to decreased training effectiveness with a changing number of nodes in IoT. Furthermore, the heterogeneity of data in IoT nodes also affects the training effectiveness due to the generalization limitations of SL. Therefore, we propose a model distributed training method based on improved SL in IoT. Firstly, we leverage Mixup, which is a data augmentation method to blend the “smashed data” generated by client models, thereby creating more diverse samples with smooth transitions in the feature space. This improves robustness against the influence of node number fluctuations. Secondly, a hypernetwork is employed to allocate weights to model parameters, obtaining personalized training parameters for each client model by evaluating the importance of different clients, thus improving the impact of heterogeneous node data on model generalization performance. Experimental results show that the proposed method outperforms other distributed training methods on various datasets in terms of model performance.