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 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.
Highlights
The proliferation of smart devices, mobile networks and computing technology have sparked a new era of Internet of Things (IoT), which is poised to make substantial advances in all aspects of our modern life, including smart healthcare system, intelligent transportation infrastructure, etc [1]
PERSONALIZED FEDERATED LEARNING MECHANISMS we review and elaborate several key personalized federated learning mechanisms that can be integrated with PerFit framework for intelligent IoT applications
As for personalized federated learning (FL), we study the performance of the two widelyadopted approaches: federated transfer learning (FTL) and federated distillation (FD)
Summary
The proliferation of smart devices, mobile networks and computing technology have sparked a new era of Internet of Things (IoT), which is poised to make substantial advances in all aspects of our modern life, including smart healthcare system, intelligent transportation infrastructure, etc [1]. With huge amounts of smart devices connected together in IoT, we are able to get access to massive user data to yield insights, train task-specified machine learning models and utimately provide high-quality smart services and products. To reap the benefits of IoT data, the predominant approach is to collect scattered user data to a central cloud for modeling and transfer the trained model to user devices for task inferences. This kind of approach can be ineffective as data transmission and model transfer will result in high communication cost and latency [2]. Insufficient data samples and local data shifts will lead to an even worse model
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