  Decentralized machine learning has broadened its scope recently with the
invention of Federated Learning (FL), Split Learning (SL), and their hybrids
like Split Federated Learning (SplitFed or SFL). The goal of SFL is to reduce
the computational power required by each client in FL and parallelize SL while
maintaining privacy. This paper investigates the robustness of SFL against
packet loss on communication links. The performance of various SFL aggregation
strategies is examined by splitting the model at two points -- shallow split
and deep split -- and testing whether the split point makes a statistically
significant difference to the accuracy of the final model. Experiments are
carried out on a segmentation model for human embryo images and indicate the
statistically significant advantage of a deeper split point.
