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

• URL: http://arxiv.org/abs/2002.10671v3
• Date: Sat, 2 May 2020 08:44:54 GMT
• Title: Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
• Authors: Qiong Wu and Kaiwen He and Xu Chen
• Abstract summary: Internet of Things (IoT) have widely penetrated in different aspects of modern life. In this article we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications.
• Score: 12.199870302894439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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