Abstract
Artificial intelligence of things (AIoT) is an emerging paradigm integrating artificial intelligence (AI) technologies within the Internet of Things (IoT) paradigm. However, deploying deep-learning models on IoT devices is challenging due to their inherent computational, communications, and security constraints. To address these challenges, we propose Flexible Neuron Selection ( FlexNS ), a computation- and communication-efficient personalised multi-task transfer learning framework for AIoT. FlexNS enables IoT devices to train their private task-specific shallow models by leveraging a multi-task, deep-learning model pre-trained by a cloud server. FlexNS significantly reduces IoT devices' computational and communications resource demands by selecting a subset of neurons in an early layer of the server's public model to be connected to the private models of multiple IoT devices. The neurons need to be carefully selected to ensure effective and efficient knowledge transfer to the fine-tuned private models tailored to each IoT device's specific task. Experimental results show that FlexNS -based private models achieve 104.3% and 98.4% model accuracy compared to the public model for two datasets on network intrusion detection and image classification tasks, with 99.5% and 98.0% reduction in training and inference time.