In the resource-constrained IoT-edge computing environment, Split Federated (SplitFed) learning is implemented to enhance training efficiency. This method involves each terminal device dividing its full DNN model at a designated layer into a device-side model and a server-side model, then offloading the latter to the edge server. However, existing research overlooks four critical issues as follows: (1) the heterogeneity of end devices' resource capacities and the sizes of their local data samples impact training efficiency; (2) the influence of the edge server's computation and network resource allocation on training efficiency; (3) the data leakage risk associated with the offloaded server-side sub-model; (4) the privacy drawbacks of current centralized algorithms. Consequently, proactively identifying the optimal cut layer and server resource requirements for each end device to minimize training latency while adhering to data leakage risk rate constraint remains a challenging issue. To address these problems, this paper first formulates the latency and data leakage risk of training DNN models using Split Federated learning. Next, we frame the Split Federated learning problem as a mixed-integer nonlinear programming challenge. To tackle this, we propose a decentralized Proactive Model Offloading and Resource Allocation (DP-MORA) scheme, empowering each end device to determine its cut layer and resource requirements based on its local multidimensional training configuration, without knowledge of other devices' configurations. Extensive experiments on two real-world datasets demonstrate that the DP-MORA scheme effectively reduces DNN model training latency, enhances training efficiency, and complies with data leakage risk constraints compared to several baseline algorithms across various experimental settings.

翻译：在资源受限的物联网边缘计算环境中，拆分联邦学习被应用于提升训练效率。该方法要求每个终端设备将其完整的深度神经网络模型在指定分割层划分为设备端模型与服务器端模型，并将后者卸载至边缘服务器。然而，现有研究忽视了以下四个关键问题：(1) 终端设备资源能力的异构性及其本地数据样本规模对训练效率的影响；(2) 边缘服务器计算与网络资源分配对训练效率的作用；(3) 卸载的服务器端子模型所关联的数据泄露风险；(4) 现有集中式算法的隐私缺陷。因此，如何在满足数据泄露风险率约束的前提下，主动为每个终端设备确定最优分割层与服务器资源需求以最小化训练延迟，仍是一个具有挑战性的难题。为应对这些问题，本文首先形式化分析了采用拆分联邦学习训练深度神经网络模型的延迟与数据泄露风险。接着，我们将拆分联邦学习问题构建为混合整数非线性规划问题。针对此问题，我们提出一种去中心化的主动模型卸载与资源分配方案，使每个终端设备能够依据其本地多维训练配置自主确定分割层与资源需求，而无需知晓其他设备的配置信息。基于两个真实数据集的广泛实验表明，在不同实验设置下，相较于多种基线算法，所提方案能有效降低深度神经网络模型训练延迟、提升训练效率，并满足数据泄露风险约束。