Title: Convergence Analysis of Split Learning on Non-IID Data. Abstract: Split Learning (SL) is one promising variant of Federated Learning (FL), where the AI model is split and trained at the clients and the server collaboratively. By offloading the computation-intensive portions to the server, SL enables efficient model training on resource-constrained clients. Despite its booming applications, SL still lacks rigorous convergence analysis on non-IID data, which is critical for hyperparameter selection. In this paper, we first prove that SL exhibits an $\mathcal{O}(1/\sqrt{T})$ convergence rate for non-convex objectives on non-IID data, where $T$ is the number of total steps. By comparing the convergence analysis and experimental results, SL can outperform FL in terms of convergence rate (w.r.t. per-client training/communication rounds, and hence, the computation efficiency) and exhibit comparable accuracy to FL on mildly non-IID data. In contrast, FL prevails on highly non-IID data.