In this paper, we propose a novel distributed learning scheme, named group-based split federated learning (GSFL), to speed up artificial intelligence (AI) model training. Specifically, the GSFL operates in a split-then-federated manner, which consists of three steps: 1) Model distribution, in which the access point (AP) splits the AI models and distributes the client-side models to clients; 2) Model training, in which each client executes forward propagation and transmit the smashed data to the edge server. The edge server executes forward and backward propagation and then returns the gradient to the clients for updating local client-side models; and 3) Model aggregation, in which edge servers aggregate the server-side and client-side models. Simulation results show that the GSFL outperforms vanilla split learning and federated learning schemes in terms of overall training latency while achieving satisfactory accuracy.

翻译：本文提出一种名为基于组的分割联邦学习（GSFL）的新型分布式学习方案，旨在加速人工智能（AI）模型训练。具体而言，GSFL采用先分割再联邦的运行方式，包含三个步骤：1)模型分发——接入点（AP）分割AI模型并将客户端侧模型分发给各客户端；2)模型训练——每个客户端执行前向传播并将压缩数据（smashed data）传输至边缘服务器，边缘服务器执行前向与反向传播后将梯度返回给客户端以更新本地客户端侧模型；3)模型聚合——边缘服务器聚合服务器侧与客户端侧模型。仿真结果表明，GSFL在整体训练延迟方面优于传统分割学习与联邦学习方案，同时能达到令人满意的精度。