  Although Split Federated Learning (SFL) is good at enabling knowledge sharing
among resource-constrained clients, it suffers from the problem of low training
accuracy due to the neglect of data heterogeneity and catastrophic forgetting.
To address this issue, we propose a novel SFL approach named KoReA-SFL, which
adopts a multi-model aggregation mechanism to alleviate gradient divergence
caused by heterogeneous data and a knowledge replay strategy to deal with
catastrophic forgetting. Specifically, in KoReA-SFL cloud servers (i.e., fed
server and main server) maintain multiple branch model portions rather than a
global portion for local training and an aggregated master-model portion for
knowledge sharing among branch portions. To avoid catastrophic forgetting, the
main server of KoReA-SFL selects multiple assistant devices for knowledge
replay according to the training data distribution of each server-side
branch-model portion. Experimental results obtained from non-IID and IID
scenarios demonstrate that KoReA-SFL significantly outperforms conventional SFL
methods (by up to 23.25\% test accuracy improvement).
