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

📝 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 $.

💡 Analysis

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 $.

📄 Content

1 Hierarchical Federated Graph Attention Networks for Scalable and Resilient UAV Collision Avoidance Rathin Chandra Shit and Sharmila Subudhi 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(n2) 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 architec- ture. We spread the intelligence into three layers: an immediate collision avoiding local layer running on dense graph attention with latency of < 10ms, a regional layer using sparse attention with O(nk) computational complexity and asynchronous feder- ated 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 ∈[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 95th 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. Index Terms—UAV Collision Avoidance, Hierarchical Feder- ated Learning, Graph Attention Networks, Adaptive Privacy, Byzantine Resilience, Scalability I. INTRODUCTION Large swarms of Unmanned Aerial Vehicle (UAV) in com- mon airspace create highly demanding safety and security issues with tight real-time requirements. Under adversarial conditions, collision avoidance should work in sub-50ms (from here onward, ms be interpreted as milliseconds) decision win- dows with malicious nodes that might seek to jam the commu- nication, impute false data or poison individual UAVs through a gradient poisoning attack [1], [2]. Although Federated Learn- ing (FL) and Graph Attention Networks (GATs) have potential to be distributed coordination tools, a straightforward mixture of the two can produce computationally expensive O(n2) optimization procedures which can only cooperate with 50 UAVs [3], [4]. ORCA (Optimal Reciprocal Collision Avoidance) [5] and RVO2 (Reciprocal Velocity Obstacle) [6] are classical imple- mentation methods that paint geometric collision avoidance with collision avoidance certainties. However, they do not have the capacity of learning advanced scenarios. Recent deep reinforcement learning solution developed by Wang et al. [7] provides a better performance, but in need of a centralized Rathin Chandra Shit, Ph.D. (e-mail: rathin088@gmail.com). Sharmila Subudhi, Ph.D. (e-mail: sharmilasubudhi@ieee.org). coordination [8], [9]. Thus, it introduces bottlenecks and a single point of failure while avoiding collisions. Further, Standard FL solutions have synchronization delay issues and are not Byzantine resilient [1]. Talat and Hamza [2] suggested an asynchronous FL work to enhance real- time throughput, but fails to deal with the peculiarity of collision avoidance where high latency can create issues [10]. In addition, the Graph Attention Networks (GATs) recently demonstrated the potential of graph based models in multi- agent coordination, although they exhibit quadratic complexity scaling. Sparse attention mechanisms [4] are less computa- tionally demanding but they have not been systematically considered on the real-time UAV coordination with Byzantine fault tolerance [11]. These existing frameworks have a fundamental trilemma in terms of scalability-security guarantees. To the best of our knowledge, these issues are: • They either trade off real-time performance and secu- rity guarantees (blockchain-based solutions, which must accept > 500 ms consensus to be safe and require broadcasting raw sensor data [12]), or • They scale with security guarantees (centralized ap- proaches which have to be vulnerable to single point of failure), or • They apply blockchain consensus to every collision avoidance decision. The Monolithic architectures via application of blanket secu- rity to all the interactions are resource-wasting, and often too expensive [7]. Further, the dense graph attention is too fast to be possible on large swarms [13]. Key Contributions: This paper solves the scalability- security trilemma by stratifying the architecture. We suggest the structure in the form of the hierarchy of sources of resources to perform the computation and safeguard based on the situation within operation and time demands.

1. A three-layer hierarchical s

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