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. These directions represent promising avenues for future research and development.

翻译：分离联邦学习（SFL）作为一种新兴的分布式学习技术，融合了联邦学习与分离学习的优势，既强调快速收敛的优越性，又关注隐私保护问题，因此受到工业界与学术界的广泛关注。然而，由于SFL模型需在特定层（即切割层）划分为客户端模型与服务器端模型，切割层的选择会直接影响训练负担及客户端模型的输出结果，进而对客户端的能耗与隐私产生显著影响。同时，由于客户端计算与网络能力的固有不均性，切割层的设计挑战极为复杂。本文系统梳理了SFL的完整流程，并对能耗与隐私进行了深入分析，探讨了不同系统参数对切割层选择策略的影响。此外，我们通过一个示例展示了切割层的选择过程，旨在在能耗约束条件下最小化服务器端重构原始数据的风险，这涉及多个维度的权衡。最后，我们指出了该领域面临的开放性挑战，这些方向为未来研究与发展提供了重要潜力。