Byzantine-Resilient Federated Learning With Trust-Aware Task Scheduling for Heterogeneous UAV Swarms

The deployment of unmanned aerial vehicle (UAV) swarms for collaborative intelligence in urban environments presents unprecedented opportunities for smart city applications, yet faces critical security challenges from Byzantine attacks in distributed learning scenarios. This paper presents a comprehensive framework that jointly addresses Byzantine-resilient federated learning and trust-aware task scheduling for heterogeneous UAV fleets operating under adversarial conditions. We propose a novel aggregation mechanism that employs multi-metric anomaly detection, combining gradient norm analysis, statistical residual examination, and directional alignment assessment to identify and mitigate malicious model updates. The framework incorporates a dynamic trust management system that maintains behavioral profiles of participating agents, enabling adaptive isolation of compromised nodes while preserving system performance. Furthermore, we develop a security-conscious task scheduler that considers real-time trust scores, resource constraints, and environmental threat indicators to optimize mission allocation across the swarm. Our approach uniquely couples learning objectives with operational requirements, allowing UAVs to adapt their training parameters based on perceived threat levels. Theoretical analysis establishes convergence guarantees under Byzantine presence and demonstrates trust score separation between honest and malicious agents. The proposed framework provides a practical solution for deploying secure collaborative learning in mission-critical UAV applications.