论文标题
在物联网中无处不在的情报的边缘计算中联合和拆分学习的结合：最新和未来的方向
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions
论文作者
论文摘要
联合学习（FL）和分裂学习（SL）是两种新兴的协作学习方法，可能会极大地促进物联网（IoT）中无处不在的智能。联合学习启用了机器学习（ML）模型，该模型使用私人数据汇总为全球模型。分裂学习使ML模型的不同部分可以在学习框架中对不同工人进行协作培训。联合学习和分裂学习，每个学习都有独特的优势和各自的局限性，可能会相互补充，在物联网中无处不在的智能。因此，联合学习和分裂学习的结合最近成为一个活跃的研究领域，引起了广泛的兴趣。在本文中，我们回顾了联合学习和拆分学习方面的最新发展，并介绍了一项有关最新技术的调查，该技术用于将这两种学习方法结合在基于边缘计算的物联网环境中。我们还确定了一些开放性问题，并讨论了该领域未来研究的可能方向，希望进一步引起研究界对这个新兴领域的兴趣。
Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in 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 has unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, 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 a hope to further arouse the research community's interest in this emerging field.
