BDFL: A Blockchain-Enabled FL Framework for Edge-based Smart UAV Delivery Systems
Chengzu Dong, Zhiyu Xu, Frank Jiang, Shantanu Pal, Chong Zhang, Shiping Chen, Xiao Liu
Abstract
In recent years, edge-based smart unmanned aerial vehicle (UAV) delivery systems have attracted a lot of attention by both academia and industry given its promising business value and also as an ideal testbed for many emerging technologies such as edge computing, blockchain and machine learning. At the moment, one of the critical challenges for smart UAV delivery systems is data privacy since the massive amount of data is being generated by both users and UAVs and the data is used for training machine learning models to support various smart applications such as autonomous navigation, facial recognition, and person re-identification (ReID). To tackle such a challenge, federated learning (FL) has been widely used as a promising solution since it only needs to share and update model parameters with the centralised server without transmitting the raw data. However, conventional FL still faces the issue of the single-point-of-failure. To address these issues, in this paper, we propose BDFL, a Blockchain-enabled decentralised FL framework for edge-based smart UAV delivery systems. In our framework, Blockchain provides a decentralised network for FL to eliminate the need for a centralised server and store private data in the decentralised permissioned Blockchain to avoid the single-point-of-failure. To motivate our study and analyse the privacy concerns, we employ the person ReID application in smart UAV delivery systems as a typical example. In addition, we also provide the customised proof of quality factor (cPoQF) consensus protocol to address the scalability issue of the Blockchain in order to support the increasing number of smart applications in UAV delivery system. The effectiveness of the framework is demonstrated through experiments on energy efficiency, confirmation time and throughput, with further discussion on the impact of the incentive mechanism, and the analysis of its resiliency under various security attacks.