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.

翻译：隐私性、可扩展性和可靠性是无人机网络作为分布式系统所面临的重大挑战，尤其是在采用涉及大量数据交换的机器学习技术时。近年来，联邦学习在无人机网络中的应用提升了协作性、隐私性、鲁棒性和适应性，使其成为无人机应用的一种有前景的框架。然而，为无人机网络实施联邦学习也带来了通信开销、同步问题、可扩展性限制和资源约束等缺陷。为解决这些挑战，本文提出了一种面向无人机网络的基于区块链的可聚类可扩展联邦学习（BCS-FL）框架。该框架提升了联邦学习在大规模无人机网络中的去中心化程度、协调性、可扩展性和效率。该框架将无人机网络划分为多个独立的簇，并由簇头无人机进行协调，以构建连通图。聚类机制使得对机器学习模型更新的高效协调成为可能。此外，混合的簇间与簇内模型聚合方案在每个训练轮次后生成全局模型，提升了簇间的协作与知识共享。数值结果证明了收敛性的达成，同时也强调了训练效率与通信效率之间的权衡关系。