Split Federated Learning (SFL) is a distributed machine learning paradigm that combines federated learning and split learning. In SFL, a neural network is partitioned at a cut layer, with the initial layers deployed on clients and remaining layers on a training server. There are two main variants of SFL: SFL-V1 where the training server maintains separate server-side models for each client, and SFL-V2 where the training server maintains a single shared model for all clients. While existing studies have focused on algorithm development for SFL, a comprehensive quantitative analysis of how the cut layer selection affects model performance remains unexplored. This paper addresses this gap by providing numerical and theoretical analysis of SFL performance and convergence relative to cut layer selection. We find that SFL-V1 is relatively invariant to the choice of cut layer, which is consistent with our theoretical results. Numerical experiments on four datasets and two neural networks show that the cut layer selection significantly affects the performance of SFL-V2. Moreover, SFL-V2 with an appropriate cut layer selection outperforms FedAvg on heterogeneous data.

翻译：分割联邦学习（SFL）是一种结合了联邦学习与分割学习的分布式机器学习范式。在SFL中，神经网络在某个切层处被分割，初始层部署在客户端，剩余层部署在训练服务器上。SFL主要有两种变体：SFL-V1（训练服务器为每个客户端维护独立的服务器端模型）和SFL-V2（训练服务器为所有客户端维护一个共享模型）。尽管现有研究主要关注SFL的算法开发，但关于切层选择如何影响模型性能的全面定量分析仍有待探索。本文通过提供关于SFL性能及收敛性相对于切层选择的数值与理论分析，填补了这一空白。我们发现SFL-V1的性能对切层选择相对不敏感，这与我们的理论结果一致。在四个数据集和两种神经网络上的数值实验表明，切层选择显著影响SFL-V2的性能。此外，在异构数据上，采用适当切层选择的SFL-V2其表现优于FedAvg。