Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
This addresses the problem of deploying federated learning in heterogeneous IoT environments for intelligent applications, but it appears incremental as it builds on existing personalized federated learning methods.
The paper tackles the challenge of device, statistical, and model heterogeneities in federated learning for IoT applications by proposing a personalized federated learning framework in a cloud-edge architecture, demonstrating its effectiveness through a case study on human activity recognition.
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.