  Federated Learning (FL) and Split Learning (SL) are two popular paradigms of
distributed machine learning. By offloading the computation-intensive portions
to the server, SL is promising for deep model training on resource-constrained
devices, yet still lacking of rigorous convergence analysis. In this paper, we
derive the convergence guarantees of Sequential SL (SSL, the vanilla case of SL
that conducts the model training in sequence) for strongly/general/non-convex
objectives on heterogeneous data. Notably, the derived guarantees suggest that
SSL is better than Federated Averaging (FedAvg, the most popular algorithm in
FL) on heterogeneous data. We validate the counterintuitive analysis result
empirically on extremely heterogeneous data.
