Hierarchical Federated Graph Attention Networks for Scalable and Resilient UAV Collision Avoidance

TL;DR

The paper tackles the challenge of scalable, privacy-preserving, and Byzantine-resilient UAV collision avoidance in large swarms. It introduces HF-GAT, a three-layer hierarchy combining dense local graph attention for ultra-low-latency reactions, sparse regional attention with asynchronous Byzantine-resilient federated learning for scalable coordination, and a Hashgraph-inspired global consensus with a DHT-based audit for fast, auditable decisions. Key contributions include an adaptive differential privacy mechanism, coordinate-wise TrimmedMean aggregation with $f < n/3$ resilience, sub-quadratic scaling $O(nk)$, and a lightweight audit paradigm that reduces overhead by about 85% compared with blockchain approaches. Experimental results on up to 500 UAVs show sub-50 ms decision latency and collision rates below $2\%$ under normal operation and below $6\%$ under adversarial conditions, highlighting practical feasibility for large-scale autonomous swarms.

Abstract

The real-time performance, adversarial resiliency, and privacy preservation are the most important metrics that need to be balanced to practice collision avoidance in large-scale multi-UAV (Unmanned Aerial Vehicle) systems. Current frameworks tend to prescribe monolithic solutions that are not only prohibitively computationally complex with a scaling cost of $O(n^2)$ but simply do not offer Byzantine fault tolerance. The proposed hierarchical framework presented in this paper tries to eliminate such trade-offs by stratifying a three-layered architecture. We spread the intelligence into three layers: an immediate collision avoiding local layer running on dense graph attention with latency of $<10 ms$, a regional layer using sparse attention with $O(nk)$ computational complexity and asynchronous federated learning with coordinate-wise trimmed mean aggregation, and lastly, a global layer using a lightweight Hashgraph-inspired protocol. We have proposed an adaptive differential privacy mechanism, wherein the noise level $(ε\in [0.1, 1.0])$ is dynamically reduced based on an evaluation of the measured real-time threat that in turn maximized the privacy-utility tradeoff. Through the use of Distributed Hash Table (DHT)-based lightweight audit logging instead of heavyweight blockchain consensus, the median cost of getting a $95^{th}$ percentile decision within 50ms is observed across all tested swarm sizes. This architecture provides a scalable scenario of 500 UAVs with a collision rate of $< 2.0\%$ and the Byzantine fault tolerance of $f < n/3$.

Figures (4)

• Figure 1: Complexity Analysis and Hierarchical Architecture. The three levels of decision-making are nested in each other at escalating scopes of operation and security assurances. Layer 1 adopts the mechanism of dense attention $O(n^2)$ on local neighborhoods, Layer 2 adopts the sparse attention $O(nk)$ on a regional coordination, and Layer 3 offers a global consensus with DHT-based audit trails.
• Figure 2: Decision latency comparison across different swarm sizes
• Figure 3: Performance under Byzantine adversarial attacks
• Figure 4: Adaptive privacy mechanism performance under varying threat levels