Efficient edge AI implementation for IoT device identification for hierarchical federated learning

Abstract

As IoT devices proliferate, efficient IoT device identification is crucial for resource management, planning, and detecting anomalous traffic. Traditional ML-based identification relies on centralised training, but federated learning (FL) offers a privacy-preserving alternative, enabling collaborative model training without sharing raw data. FL enhances edge devices' ability to identify previously unconnected devices. However, resource constraints like limited computation, power, and communication capabilities may prevent some edge devices from actively participating in FL. We propose a solution where resource-limited IoT devices benefit from FL by subscribing to server-based services. This work presents an efficient AI model implementation for IoT device identification on embedded edge devices, detailing the toolflow from model generation to hardware implementation. We apply and evaluate various model optimisation techniques to balance performance and resource trade-offs, offering insights to advance edge-AI and scalable FL-based ML applications for IoT networks.