The development of artificial intelligence (AI) provides opportunities for the promotion of deep neural network (DNN)-based applications. However, the large amount of parameters and computational complexity of DNN makes it difficult to deploy it on edge devices which are resource-constrained. An efficient method to address this challenge is model partition/splitting, in which DNN is divided into two parts which are deployed on device and server respectively for co-training or co-inference. In this paper, we consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL). We consider a practical scenario of heterogeneous devices with individual split points of DNN. We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. By using alternating optimization, we decompose the problem into two sub-problems and solve them optimally. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement.

翻译：人工智能（AI）的发展为推进基于深度神经网络（DNN）的应用提供了机遇。然而，DNN具有参数量大、计算复杂度高等特点，难以直接部署在资源受限的边缘设备上。一种有效的解决方案是模型分区/拆分，即将DNN划分为两部分，分别部署在设备端和服务器端进行协同训练或协同推理。本文考虑了一种结合联邦学习（FL）并行模型训练机制与分裂学习（SL）模型拆分结构的分裂联邦学习（SFL）框架。我们针对异构设备具有个性化DNN拆分点的实际场景，构建了联合优化拆分点选择与带宽分配的问题以最小化系统时延。通过交替优化方法，将该问题分解为两个子问题并求解最优解。实验结果表明，本工作在降低时延和提升精度方面具有显著优势。