Energy Efficient User Scheduling for Hybrid Split and Federated Learning in Wireless UAV Networks

The use of unmanned aerial vehicles (UAVs) as flying users provides various applications by exploiting machine learning (ML) algorithms. Recently, distributed learning algorithms, federated learning (FL) and split learning (SL), have been exploited to train ML models distributedly via sharing model parameters rather than large raw datasets in the conventional centralized learning algorithms. Considering the diversity of users with heterogeneous resources, computation capabilities, and data distributions, we propose a hybrid split and federated learning (HSFL) framework that allows users to select either split training (ST) or federated training (FT) method based on the characteristics of the users in wireless UAV networks. Due to unreliable wireless channels and the limited energy of the users, we further formulate a user scheduling and training method selection problem within HSFL framework as a Multiple-Choice Knapsack Problem (MCKP) and propose an energy-efficient user scheduling algorithm to select a subset of users in each round and schedule each user with either ST or FT method. The simulations demonstrate that our proposed HSFL framework consumes less energy while having the same good test accuracy performance compared to the currently distributed learning algorithms, and the proposed user scheduling algorithm achieves energy-efficient selection of ST or FT method under different distributions.