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).

提出了一种名为 KoReA-SFL 的新型 Split Federated Learning 方法，通过多模型聚合机制减轻因异构数据引起的梯度发散，并采用知识重放策略解决灾难性遗忘问题。KoReA-SFL 在云服务器中维护多个分支模型部分，用于局部训练和分支部分之间的知识共享，在非独立同分布和独立同分布的情况下实验结果表明，KoReA-SFL 相较于传统的 SFL 方法具有显著的测试准确率提升（最高提升 23.25%）。