ATLAS-HFL: Learning-Aware Anchor Graphs for Closed-Loop Federated Learning in Dynamic UAV Swarms

Dynamic UAV swarms must support federated task learning under changing topology, limited resources, heterogeneous data, and intermittent cluster-head failures. Existing UAV clustering methods usually treat the cluster structure as a communication route or aggregation scaffold. However, in dynamic UAV federated learning, a useful organization structure should encode not only physical feasibility and resource sustainability, but also task relations that guide future model cooperation. The observations suggest that swarm organization should be maintained as an evolving learning-aware state rather than a one-time clustering result. Following this reasoning, a hierarchical federated learning framework called ATLAS-HFL is developed through a sparse anchor graph. ATLAS-HFL represents UAVs with physical states, resource states, and compact local learning states without constructing a full pairwise UAV graph. The learned UAV-to-anchor associations determine dynamic clusters, support primary-backup cluster-head election, and produce task-aware inter-cluster cooperation weights for cluster-specific initialization in the next control period. Updated local training states then refine the anchor graph, forming a closed loop between federated learning and swarm organization. Experiments across heterogeneous federated learning settings, cluster-head failure scenarios, scalability tests, and network simulations show that ATLAS-HFL improves task performance, preserves service availability under cluster-head failures, and enhances communication reliability while maintaining comparable network lifetime.