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.

本文提出了一种基于云边架构的个性化联邦学习框架，以解决在物联网环境下的设备、统计和模型异构性，能够为智能物联网应用提供高效时延和低延迟的处理能力，并使用人类活动识别案例研究证明了个性化联邦学习在智能物联网应用中的有效性。