HFedDI: A novel privacy preserving horizontal federated learning based scheme for IoT device identification

Abstract

As the number of IoT devices that are getting connected to the Internet is increasing, there is a need to automatically detect the IoT devices connected to the network for efficient network resource management and planning, identification of security attacks and anomalies in the network. Centralized machine learning techniques for device identification lead to the use of excessive communication bandwidth and can at times lead to privacy issues as data has to be transported to the central location. In this paper, we propose HFedDI- a novel horizontal learning based federated learning scheme for IoT device identification. The proposed federated learning technique is scalable as an edge device that does device identification can identify the devices which were never connected to it previously due to the achieved generalization accuracy as a result of model updates from other edge devices. Our work has indicated significant performance improvement in device identification across IID scenarios, and non-IID (specifically tested for data and label distribution skew) scenarios when tested on three publically available datasets. The proposed device identification technique is privacy preserving and is promising as the existing work in literature which utilized federated learning for other applications has indicated very poor results under non-IID label skew scenarios.