Multi-Agent Federated Edge Learning for UAV-IDS in Smart City IoT Environment

Unmanned Aerial Vehicles (UAVs) have become integral to modern consumer electronics (CE) ecosystem-based smart city infrastructures, providing dynamic connectivity, environmental monitoring, and situational awareness through distributed consumer Internet of Things (IoT) networks. However, their reliance on wireless communication links exposes them to a wide range of cyber and physical-layer attacks, demanding intelligent yet lightweight defense mechanisms. This paper presents aMulti-Agent Federated Edge Intrusion Detection System(UAV-IDS) designed to ensure privacy-preserving, real-time protection for UAV-enabled smart city IoT environments. The proposed framework integrates a hybrid Autoencoder–LSTM architecture deployed across edge nodes, where multiple intelligent agents collaboratively perform feature extraction, anomaly detection, and policy adaptation. A UAV-based coordinator manages federated learning rounds, aggregating model updates from distributed edge nodes without sharing raw data. Extensive experiments conducted on the T-ITS dataset demonstrate that the proposed system achieves 99.44% accuracy, 99.31% precision, and a 46.8 ms average detection latency, outperforming existing deep and federated IDS frameworks. Moreover, the lightweight deployment (8.42 MB) and low communication cost confirm the feasibility of the proposed design for embedded UAV operations. The results validate the framework’s potential as an adaptive, scalable, and privacy-conscious solution for next-generation smart city security infrastructures.