Next-Gen Drone Swarm Coordination via Federated Learning

Abstract

The rapid evolution of unmanned aerial vehicle (UAV) technology has ushered in an era in which cooperative drone swarms can execute complex, large‑scale missions across diverse civilian, commercial, and defense sectors. Traditional centralized coordination frameworks, however, struggle to meet the demands of these dynamic, resource‑constrained environments due to their limited scalability, single points of failure, and privacy vulnerabilities. This manuscript introduces Next‑Gen Drone Swarm Coordination via Federated Learning (DSC‑FL), a hierarchical, privacy‑preserving architecture that enables real‑time, collaborative decision‑making among heterogeneous UAVs. By dynamically clustering drones according to proximity and mission role, DSC‑FL balances local compute, communication overhead, and model accuracy. Within each cluster, drones perform local model training on sensor and state data, encrypt gradient updates via secure aggregation protocols, and transmit only aggregated model deltas to higher‑level aggregators—minimizing bandwidth consumption and protecting raw data. A consensus‑based flight‑control algorithm fuses federated model predictions across neighbors to adapt formations, avoid obstacles, and respond to environmental disturbances. We evaluate DSC‑FL in a high‑fidelity simulation featuring urban canyons, wind gusts, intermittent link failures, and adversarial perturbations. Our results demonstrate a 25% improvement in formation‑maintenance accuracy and a 30% reduction in communication bandwidth compared to centralized and peer‑to‑peer baselines, while maintaining resilience against gradient‑inversion attacks. These findings establish DSC‑FL as a scalable, secure, and adaptable solution for next‑generation drone swarm deployments.