  Recent advancements in decentralized learning, such as Federated Learning
(FL), Split Learning (SL), and Split Federated Learning (SplitFed), have
expanded the potentials of machine learning. SplitFed aims to minimize the
computational burden on individual clients in FL and parallelize SL while
maintaining privacy. This study investigates the resilience of SplitFed to
packet loss at model split points. It explores various parameter aggregation
strategies of SplitFed by examining the impact of splitting the model at
different points-either shallow split or deep split-on the final global model
performance. The experiments, conducted on a human embryo image segmentation
task, reveal a statistically significant advantage of a deeper split point.
