SWORDFISH closes the swarm communication congestion gap by embedding a post-quantum-secure, adaptive coordination layer directly into the swarm autonomy fabric to keep perception models and role-aware autonomy synchronized without interrupting operations.

Heterogeneous unmanned aircraft swarms promise richer ISR, strike coordination, and logistics capabilities, but in GPS-denied and communications-contested environments their operational cohesion collapses. Mixed airframes, divergent autonomy models, mismatched radios, and uneven compute/battery reserves make synchronization, software updates, and real-time coordination far more fragile than in homogeneous fleets. In DARPA OFFSET field trials, communications congestion forced drones to wait minutes before joining the mission, while role rigidity left ISR and relay coverage gaps as some drones are lost, degraded, or otherwise taken out of the mission. These vulnerabilities are magnified under bandwidth limits, jamming, or spoofing which are conditions that characterize near-peer contested airspace and Agile Combat Employment (ACE) scenarios.

Our proposed concept closes this gap by embedding a post-quantum-secure, adaptive coordination layer directly into the swarm autonomy fabric. Group 1–3 UAS dynamically organize into 10–25 unit clusters with rotating relay or quorum leaders, eliminating single points of failure. Reinforcement-learning agents continually re-shape the swarm into randomized Fibonacci-sphere formations to maximize coverage and frustrate adversarial targeting, while role-aware autonomy reassigns ISR, relay, and reserve duties on-the-fly to extend mission life. Our “SwarmModelPatch” protocol pushes incremental deep-learning model deltas across the mesh in compact bursts, keeping perception models synchronized without interrupting operations.

SWORDFISH is built on Post Quantum Labs’ (PQL) proven autonomy and cybersecurity stack which combines CNSA 2.0–compliant post-quantum encryption for stealthy, low-latency communications, multi-agent reinforcement learning for adaptive role switching, and graph neural networks for real-time sensor fusion. This stack has over 15,000 hours of development, with core components validated in fielded prototypes and commercial deployments. SWORDFISH extends this foundation with federated autonomy propagation and vendor-agnostic interoperability, enabling heterogeneous swarms to coordinate securely, adapt instantly, and survive in degraded, contested conditions aligned with AFSOC’s Adaptive Airborne Enterprise (A2E) vision and AFRL swarm autonomy objectives.

For more information, technical briefings, or contracting opportunities, please contact Post Quantum Labs at [email protected].