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）近年来作为一种有前景的分布式学习技术出现，它融合了联邦学习与分割学习的优势，强调快速收敛的同时应对隐私问题。因此，这一创新受到了工业界和学术界的广泛关注。然而，由于SFL在特定层（称为切割层）将模型分割为客户端侧模型和服务器侧模型，切割层的选择会对客户端的能耗及其隐私产生显著影响，因为它决定了训练负担和客户端侧模型的输出。本文对SFL过程进行了全面概述，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切割层选择策略的影响。此外，我们提供了一个切割层选择的示例，旨在在将能耗维持在所需预算内的同时，最小化客户端在服务器端重建原始数据的风险，这涉及权衡。最后，我们讨论了该领域的开放性挑战。这些方向代表了未来研究与发展的有前景的途径。