The Data Value Based Asynchronous Federated Learning for UAV Swarm Under Unstable Communication Scenarios

Federated learning has provided a new approach to coordinating a group of clients to train a machine learning model collaboratively, which can be easily embedded into Unmanned Aerial Vehicle (UAV) swarms. Compared with the terrestrial wireless networks, the UAV swarm faces more precarious communication conditions, rendering synchronous aggregation no longer tenable. Additionally, the data collected from UAVs tend to be heterogeneous due to different deployment regions or requirements. To overcome these restrictions, this paper has proposed a novel two-stage Asynchronous Federated Learning scheme for the UAV swarm. Initially, the convergence property of both convex and non-convex models trained by the proposed scheme is analyzed. In the pre-training stage, we modeled the learning process as a cooperative game with demonstrated monotonicity and submodularity. Furthermore, the Shapley Value is imported to quantify data values of UAVs, and the upper bound of its estimation error rate is derived. In the training stage, a new concept named Network Age of Updates (AoU) is proposed to address the fairness issue, quantifying the model's generalization capability with data value consideration, and a sequential UAV selection scheduling is performed through the AoU minimization by Whittle Index method. Finally, the system performance is validated through both theoretical analysis and simulations.