Participant and Sample Selection for Efficient Online Federated Learning in UAV Swarms

Federated learning (FL) as an emerging distributed machine learning (ML) paradigm enables participants to train their on-device data locally and share model parameters with others by the parameter server. Differing from the centralized ML, FL splits the high requirements of training data and computing power from the server to clients, which is well adapted to unmanned aerial vehicle (UAV) swarms with scattered nodes, heterogeneous data, and limited computing power. However, pre-trained models are unsatisfactory in unfamiliar scenes and most existing approaches fail to concentrate on the communication-sensitivity and real-time requirements in UAV-enabled FL scenarios. To address this problem, this paper proposes participant and sample selection for efficient online federated learning in UAV swarms (FedOL). Through the combination of online learning and FL, UAVs can supplement real-time samples and quickly improve the model accuracy in unfamiliar scenes. Meanwhile, to reduce the training latency with expected model accuracy, FedOL allows the server UAV to select participants with high training utility, while the client UAVs select more important samples. We implement FedOL and deploy it on UAV embedded devices. Experimental results show that compared with existing FL approaches, FedOL speeds up by about 2.61× and reaches the final accuracy about 1.02× higher.