FedShufde: A privacy preserving framework of federated learning for edge-based smart UAV delivery system

Abstract

In recent years, there has been a rapid increase in the integration of Internet of Things (IoT) systems into edge computing. This integration offers several advantages over traditional cloud computing, including lower latency and reduced network traffic. In addition, edge computing facilitates the protection of users’ sensitive data by processing it at the edge before transmitting it to the cloud using techniques such as Federated Learning (FL) and Differential Privacy (DP). However, these techniques have limitations, such as the risk of user information being obtained by attackers through the uploaded weights/model parameters in FL and the randomness of DP, which limits data availability. To address these issues, this paper proposes a framework called FedShufde (Federated Learning with a Shuffle Model and Differential Privacy in Edge Computing Environments) to protect user privacy in edge computing-based IoT systems, using an Unmanned Aerial Vehicle (UAV) delivery system as an example. FedShufde uses local differential privacy and the shuffle model to prevent attackers from inferring user privacy from information such as UAV’s location, flight conditions, or delivery address. In addition, the network connection between the UAV and the edge server cannot be obtained by the cloud aggregator, and the shuffle model reduces the communication cost between the edge server and the cloud aggregator. Our experiments on a real-world edge-based smart UAV delivery system using public datasets demonstrate the significant advantages of our proposed framework over baseline strategies.