Stable federated learning method for low-altitude IoT networks based on election strategy - SHEN Lingfeng; WANG Guanghui; BAI Tianshui; ZHU Zhengyu; ZHANG Qiankun (物联网学报)
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 without sharing sensitive data. However, the federated learning performance is unstable because of various application scenarios, 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, significantly 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 federated 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, secure, and stable federated learning in low-altitude IoT networks.