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 paper 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 heterogeneities 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.

中文翻译：

---

智能物联网应用的个性化联合学习：基于云边缘的框架。

物联网（IoT）已广泛渗透到现代生活的各个方面，并且出现了许多智能IoT服务和应用程序。最近，提出了联合学习以通过在IoT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训练全局共享模型。然而，复杂物联网环境中固有的设备，统计和模型异质性给传统的联合学习带来了巨大挑战，使其不适合直接部署。在本文中，我们提倡在云边缘架构中针对智能物联网应用的个性化联合学习框架。为了应对物联网环境中的异构性问题，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异质性带来的负面影响。借助边缘计算的强大功能，还可以满足智能物联网应用中对快速处理能力和低延迟的要求。我们最终提供了一个基于IoT的人类活动识别的案例研究，以证明个性化联合学习对智能IoT应用的有效性。