论文标题
智能物联网应用程序的个性化联合学习：基于云边缘的框架
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
论文作者
论文摘要
物联网（物联网）在现代生活的不同方面广泛渗透，许多智能的物联网服务和应用正在出现。最近，提议联合学习通过在物联网设备上利用大量用户生成的数据样本，同时防止数据泄漏来培训全球共享的模型。但是，复杂的物联网环境中固有的设备，统计和模型异质性对传统的联邦学习构成了巨大挑战，因此不适合直接部署。在本文中，我们主张一个个性化的联合学习框架，以用于智能物联网应用程序的云边缘体系结构。为了应对物联网环境中的异质性问题，我们研究了新兴的个性化联合学习方法，这些方法能够减轻不同方面的异质性引起的负面影响。凭借边缘计算的力量，还可以实现对快速处理能力和智能物联网应用程序中低潜伏期的要求。我们最终提供了基于物联网的人类活动识别的案例研究，以证明对智能物联网应用的个性化联合学习的有效性。
Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in the complex IoT environments pose great challenges to traditional federated learning, making it unsuitable to be directly deployed. In this article we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, we investigate emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneity in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications.
