随着无人机(UAV,unmanned aerial vehicle)与物联网(IoT,Internet of things)技术的深度融合,低空物联网中传输了大量包含敏感信息的数据,存在严重的隐私泄露风险.联邦学习(FL,federated learning)允许多个参与者共同训练模型而无须共享敏感数据,为低空物联网安全应用提供了隐私保护的方案.但是,随着应用场景越来越丰富,节点异构性、网络动态性等特点导致低空物联网下的联邦学习非常不稳定.提出了一种结合Raft选举算法和权重计算的新型联邦学习方法(FedPRE-W,federated fearning based on proxy Raft election and weight calculation),提高了联邦学习的稳定性和效率.针对遮挡、网络动态变化以及节点能量耗尽等导致的代理设备中断问题,通过Raft选举算法选举新的代理设备,保障联邦学习的稳定性.结合节点异构性,通过计算节点权重,选举性能强的节点当选代理,提升了联邦学习的效率.最后,在公开数据集上对所提方法进行验证,结果显示,FedPRE-W算法在减少通信轮数、加速模型收敛以及提高系统稳定性等方面有显著优势.该方法为低空物联网进行安全、稳定、高效的联邦学习提供了一种可行的解决方案.
Abstract
The deep integration of UAV and Internet of things(IoT)transmits a large amount of sensitive data in the air-to-ground intelligent network,posing a serious risk of privacy leakage.The proposal of federated learning(FL)provides a privacy-preserving solution for low-altitude IoT applications,allowing multiple participants to jointly train models with-out sharing sensitive data.However,the federated learning performance is unstable because of various application sce-narios,heterogeneous nodes and dynamic environments.An federated fearning based on proxy Raft election and weight calculation(FedREP-W)method was proposed,which combined classical Raft election and weight calculation,signifi-cantly improving the stability and efficiency of federated training.To be more specific,the use of Raft to choose new agent devices keeped federated learning stable.By incorporating the concept of weight elections,the effectiveness of fed-erated learning could be enhenced by designating the most powerful node as an agent.The experimental results publicly available datasets show that the proposed strategy and algorithm perform well in lowering the number of communication rounds,speeding up model convergence,and making the system stable.This provides a feasible solution for efficient,se-cure,and stable federated learning in low-altitude IoT networks.
维普
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