BlockFedL: a blockchain-based federated learning framework for securing smart UAV delivery systems at the edge

The integration of edge computing into advanced UAV delivery systems is of great interest to both research and industry. This integration offers new business opportunities and serves as a testbed for innovative technologies like edge computing, blockchain, and machine learning. A key concern for these systems is data privacy, especially given the large amounts of user and UAV data processed for tasks such as self-guided navigation, facial recognition, and person re-identification (ReID). To address this, federated learning (FL) has emerged as a popular choice, allowing for model parameter sharing while keeping raw data private. However, traditional FL approaches are vulnerable to single points of failure. Our study introduces the ‘blockchain-powered edge FL’ (BlockFedL) framework, a blockchain-enhanced, decentralized FL framework for edge-based UAV delivery systems. BlockFedL leverages blockchain to form a decentralized FL network, ensuring secure data storage and mitigating risks. We specifically investigate privacy issues in the person ReID application for smart UAV delivery systems and introduce a proof of quality factor (cPoQF) consensus protocol to address blockchain scalability challenges. Experimental results demonstrate improvements in energy consumption, transaction speed, and processing capacity, highlighting the framework’s effectiveness.