Online Federated Learning on Distributed Unknown Data Using UAVs

Xichong Zhang, Haotian Xu, Yin Xu, Mingjun Xiao, Jie Wu, Jinrui Zhou

Abstract: Along with the advance of low-altitude economy, a variety of applications based on Unmanned Aerial Vehicles (UAVs) have been developed to accomplish diverse tasks. In this paper, we focus on the scenario of multiple UAVs performing Federated Learning (FL) tasks. Specifically, a group of UAVs is scheduled to repeatedly visit some Points of Interest (PoIs), collect the data produced by these PoIs, and jointly train a machine learning model based on the collected data. The most challenging issue is how to schedule UAVs to collect data so as to optimize the generalization and convergence of model training under the case that the distributions of the data produced by PoIs have not been known in advance. To address this issue, we propose a novel framework for online FL on distributed unknown data, named OFL-UD2, which is dedicated to online decision-making for UAVs to optimize model training performance. Concretely, we formulate the optimization problem while considering the convergence and quality of trained models as well as energy constraints. Then, we define a utility metric for the data quality of different PoIs and conduct a rigorous convergence analysis for OFL-UD2. Based on the analysis results, we design a two-stage algorithm to determine the scheduling of UAVs. Extensive simulations demonstrate that OFL-UD2can improve model accuracy and speed up running time compared to existing benchmarks significantly.

External IDs:dblp:conf/icde/ZhangXXXWZ25