Split Federated Learning (SFL) has recently emerged as a promising distributed learning technology, leveraging the strengths of both federated learning 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. Moreover, the design challenge of determining the cut layer is highly intricate, primarily due to the inherent heterogeneity in the computing and networking capabilities of clients. In this article, we provide a comprehensive overview of the SFL process and conduct a thorough analysis of energy consumption and privacy. This analysis takes into account 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 the risk of clients from reconstructing the raw data at the server while sustaining energy consumption within the required energy budget, which involve trade-offs. Finally, we address open challenges in this field including their applications to 6G technology. These directions represent promising avenues for future research and development.

翻译：分割联邦学习（Split Federated Learning，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。该技术强调快速收敛的优点，同时兼顾隐私问题。因此，这项创新已受到工业界和学术界的广泛关注。然而，由于模型需要在被称为切割层的特定层上分割为客户端侧和服务器侧模型，SFL中切割层的选择会对客户端的能耗及其隐私产生显著影响，因为它会改变训练负担和客户端侧模型的输出。此外，确定切割层的设计挑战极为复杂，主要源于客户端计算与网络能力的固有异构性。本文全面概述了SFL流程，并对能耗和隐私进行了深入分析。该分析考虑了各种系统参数对切割层选择策略的影响。同时，我们给出了一个切割层选择的示例，旨在最小化服务器端重构客户端原始数据的风险，同时将能耗维持在所需能耗预算内，这涉及权衡问题。最后，我们讨论了该领域面临的开放挑战，包括其在6G技术中的应用。这些方向代表了未来研究与开发中有前景的路径。