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

• Published in: IEEE computer graphics and applications Vol. 1; pp. 35 - 44
• Main Authors: Wu, Qiong, He, Kaiwen, Chen, Xu
• Format: Journal Article Magazine Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Cloud computing; Computational modeling; Customization; Data models; Edge computing; Electronic devices; Federated learning; Heterogeneity; Human activity recognition; Internet of Things; Learning systems; Moving object recognition; Network latency; Performance evaluation; personalization; Servers; Statistical methods
• ISSN: 2644-1268; 1558-1756; 2644-1268; 1558-1756
• DOI: 10.1109/OJCS.2020.2993259

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.