Split Federated Learning (SFL) has recently emerged as a promising distributed learning technology, leveraging the strengths of both federated and split learning. It emphasizes the advantages of rapid convergence while addressing privacy concerns. As a result, this innovation has received significant attention from both industry and academia. However, since the model is split at a specific layer, known as a cut layer, into both client-side and server-side models for the SFL, the choice of the cut layer in SFL can have a substantial impact on the energy consumption of clients and their privacy, as it influences the training burden and the output of the client-side models. In this article, we provide a comprehensive overview of the SFL process and thoroughly analyze energy consumption and privacy. This analysis considers the influence of various system parameters on the cut layer selection strategy. Additionally, we provide an illustrative example of the cut layer selection, aiming to minimize clients' risk of reconstructing the raw data at the server while sustaining energy consumption within the required energy budget, which involves trade-offs. Finally, we address open challenges in this field. These directions represent promising avenues for future research and development.

翻译：分割联邦学习（SFL）近期作为一种有前景的分布式学习技术出现，融合了联邦学习与分割学习的优势。它强调快速收敛的特点，同时解决隐私问题。因此，这一创新已受到工业界和学术界的广泛关注。然而，由于模型在特定层（即分割层）被划分为客户端侧模型和服务器端模型，SFL中分割层的选择会对客户端的能耗及其隐私产生显著影响，因为它会影响训练负担和客户端模型的输出。本文全面概述了SFL流程，并深入分析了能耗与隐私问题。该分析考虑了各种系统参数对分割层选择策略的影响。此外，我们提供了一个分割层选择的示例说明，旨在最小化客户端在服务器端重构原始数据的风险，同时将能耗维持在所需预算范围内，这涉及权衡问题。最后，我们探讨了该领域的开放挑战，这些方向代表了未来研究发展的潜在途径。