  Privacy, scalability, and reliability are significant challenges in unmanned
aerial vehicle (UAV) networks as distributed systems, especially when employing
machine learning (ML) technologies with substantial data exchange. Recently,
the application of federated learning (FL) to UAV networks has improved
collaboration, privacy, resilience, and adaptability, making it a promising
framework for UAV applications. However, implementing FL for UAV networks
introduces drawbacks such as communication overhead, synchronization issues,
scalability limitations, and resource constraints. To address these challenges,
this paper presents the Blockchain-enabled Clustered and Scalable Federated
Learning (BCS-FL) framework for UAV networks. This improves the
decentralization, coordination, scalability, and efficiency of FL in
large-scale UAV networks. The framework partitions UAV networks into separate
clusters, coordinated by cluster head UAVs (CHs), to establish a connected
graph. Clustering enables efficient coordination of updates to the ML model.
Additionally, hybrid inter-cluster and intra-cluster model aggregation schemes
generate the global model after each training round, improving collaboration
and knowledge sharing among clusters. The numerical findings illustrate the
achievement of convergence while also emphasizing the trade-offs between the
effectiveness of training and communication efficiency.
