论文标题
将联合学习引入物品互联网生态系统 - 初步考虑
Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations
论文作者
论文摘要
提出了联合学习（FL），以促进分布式环境中模型的培训。它支持（本地）数据隐私的保护，并使用本地资源进行模型培训。到目前为止，大多数研究一直致力于“核心问题”，例如机器学习算法对FL，数据隐私保护或处理客户之间不均匀数据分布的影响。这项贡献锚定在实际用例中，在这种情况下，将实际部署在事物生态系统中的fl。因此，在文献中发现了一些流行的考虑之外，还需要考虑一些不同的问题。此外，引入了一种构建灵活和适应性的FL解决方案的体系结构。
Federated learning (FL) was proposed to facilitate the training of models in a distributed environment. It supports the protection of (local) data privacy and uses local resources for model training. Until now, the majority of research has been devoted to "core issues", such as adaptation of machine learning algorithms to FL, data privacy protection, or dealing with the effects of uneven data distribution between clients. This contribution is anchored in a practical use case, where FL is to be actually deployed within an Internet of Things ecosystem. Hence, somewhat different issues that need to be considered, beyond popular considerations found in the literature, are identified. Moreover, an architecture that enables the building of flexible, and adaptable, FL solutions is introduced.
