Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each have unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, the combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with the hope of arousing the research community’s interest in this emerging field.

中文翻译：

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边缘计算中的联合和拆分学习在物联网中实现泛在智能：最新技术和未来方向

联邦学习 (FL) 和拆分学习 (SL) 是两种新兴的协作学习方法，可以极大地促进物联网 (IoT) 中的泛在智能。联邦学习使使用私有数据在本地训练的机器学习 (ML) 模型能够聚合到全局模型中。拆分学习允许 ML 模型的不同部分在学习框架中的不同工作人员上进行协作训练。联邦学习和分裂学习各有其独特的优势和各自的局限性，可以在物联网中相互补充，实现泛在智能。因此，联邦学习和拆分学习的结合最近成为了一个活跃的研究领域，引起了广泛的兴趣。在本文中，我们回顾了联邦学习和拆分学习的最新发展，并对在基于边缘计算的物联网环境中结合这两种学习方法的最新技术进行了调查。我们还确定了一些未解决的问题，并讨论了该领域未来研究的可能方向，以期引起研究界对这一新兴领域的兴趣。