无人机网络中基于区块链的集群化可扩展联邦学习（BCS-FL）框架
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks
AI总结：
本文提出用于无人机网络的基于区块链的集群化可扩展联邦学习（BCS-FL）框架，通过划分集群并由集群头协调更新，结合混合模型聚合方案，提升了去中心化、可扩展性和通信效率，并在实验中验证了收敛性与训练及通信效率的权衡。
摘要：
隐私、可扩展性和可靠性是无人机（UAV）网络作为分布式系统的重大挑战，尤其是在采用需要大量数据交换的机器学习（ML）技术时。近期，联邦学习（FL）在无人机网络中的应用提升了协作、隐私、弹性和适应性，使其成为无人机应用的有前景的框架。然而，在无人机网络中实施FL带来了通信开销、同步问题、可扩展性限制和资源约束等缺点。为了应对这些挑战，本文提出了用于无人机网络的基于区块链的集群化可扩展联邦学习（BCS-FL）框架。该框架提升了大规模无人机网络中FL的去中心化、协调、可扩展性和效率。该框架将无人机网络划分为独立的集群，由集群头无人机（CHs）协调，以建立连通图。集群化实现了ML模型更新高效协调。此外，混合的集群间和集群内模型聚合方案在每轮训练后生成全局模型，改善了集群间的协作和知识共享。数值结果表明实现了收敛，同时强调了训练有效性与通信效率之间的权衡。
英文摘要：
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.
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