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：在无人机网络中，作为分布式系统，隐私性、可扩展性和可靠性是重大挑战，尤其是在涉及大量数据交换的机器学习（ML）技术应用中。近年来，联邦学习（FL）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领域的一种前景广阔的框架。然而，在无人机网络中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，本文提出了面向无人机网络的区块链赋能的集群化与可扩展联邦学习（BCS-FL）框架。该框架增强了大规模无人机网络中联邦学习的去中心化性、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集群，由集群头无人机（CHs）进行协调，从而构建连通图。集群化机制实现了对机器学习模型更新的高效协调。此外，混合型跨集群与集群内部模型聚合方案在每个训练轮次后生成全局模型，提升了集群间的协作与知识共享。数值结果表明，该框架在实现收敛的同时，也体现了训练效果与通信效率之间的权衡关系。