A Digital Twin-Based Drone-Assisted Secure Data Aggregation Scheme with Federated Learning in Artificial Intelligence of Things

Abstract

Artificial intelligence of things (AIoT) has brought new promises of efficiency in our daily lives by integrating AI with the IoT. However, owing to limited resources (e.g., computational power), it is difficult to implement modern technology (e.g., AI) and improve its performance (i.e., the IoT). Moreover, cyberthreats and privacy challenges can hinder the success of the IoT. This situation is aggravated by network scarcity (i.e., limited network connectivity). This article presents a digital twin-based data aggregation scheme in which data are collected using federated learning by operating a drone and stored securely in the blockchain. Before data sharing, differential privacy is realized to enhance privacy. A multirole training scheme is proposed, along with a duplex model verification architecture using a Hampel filter and performance check. To validate the specifications, an authentication scheme was implemented by combining a cuckoo filter and timeframe check. A case study to construct an experimental environment using real hardware is discussed. Different experiments were conducted in this environment and the feasibility of the proposed scheme was validated from the outcomes.