Blockchain-Based Trustworthy and Efficient Hierarchical Federated Learning for UAV-Enabled IoT Networks

Unmanned aerial vehicles (UAVs) empowered Internet of things (IoT) networks have emerged as a burgeoning paradigm in the era of 6G. However, due to substantial data volume and privacy concerns, the conventional UAV backhaul to cloud center framework is not applicable to various latency and privacy-sensitive applications. Therefore, we propose a blockchain-based hierarchical federated learning (HFL) framework for UAV-enabled IoT networks. Specifically, we utilize the total data distance-aware device association to mitigate model impairment arising from imbalanced data distribution. Besides, we introduce a lightweight blockchain into federated learning to tackle the trust deficit caused in decentralized global model aggregation. Furthermore, we design an optimization framework that jointly orchestrating device association, wireless resource allocation, and UAV deployment, aiming at a balance between the learning latency and model accuracy. To address the formulated optimization problem, we proposed a two-stage algorithm that integrates both greedy strategy and soft actor-critic algorithm. Extensive experiments show that our proposed scheme outperforms contemporary relative to state-of-the-art alternatives.