Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework

TL;DR

This paper proposes a personalized federated learning framework within a cloud-edge architecture to address heterogeneity challenges in IoT environments, enhancing privacy, efficiency, and applicability for intelligent IoT services.

Contribution

It introduces a novel personalized federated learning framework tailored for IoT, leveraging cloud-edge computing to handle device and data heterogeneity effectively.

Findings

Personalized federated learning improves model accuracy in heterogeneous IoT settings.

The framework reduces latency and enhances processing speed through edge computing.

Case study confirms effectiveness in human activity recognition applications.

Abstract

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.