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

TL;DR

This paper introduces a hierarchical federated graph attention network framework for large-scale UAV collision avoidance, balancing real-time performance, privacy, and resilience with scalable architecture and adaptive privacy mechanisms.

Contribution

It presents a novel three-layer hierarchical architecture combining dense, sparse, and lightweight protocols for scalable, resilient, and privacy-preserving UAV collision avoidance.

Findings

Achieves <10 ms latency for local collision avoidance.

Supports 500 UAVs with collision rate <2%.

Provides Byzantine fault tolerance for f < n/3.

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 $(\epsilon \in [0.1, 1.0])$ 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 $f < n/3$.