Poster: Hierarchical Clustered Federated Learning Framework for IoT in Remote Areas

Unmanned Aerial Vehicles (UAVs) provide an effective solution for supporting Federated Learning (FL) in remote areas lacking infrastructure, but the non-IID nature of data affects FL efficiency. We propose a hierarchical clustering method with pipelined execution for UAV-assisted FL, clustering IoT devices based on computational capacity and data similarity to ensure balanced participation and faster convergence. Simulation results demonstrate improved learning efficiency and stability in non-IID environments, validating the method's effectiveness.