A new adaptive federated learning approach for privacy preserving UAV anomaly detection under non-IID distributions

Abstract

Robust and personalized anomaly detection is essential due to the rapid growth of UAV deployments in critical infrastructure, logistics, and surveillance. Distributed, non-IID, and sensitive UAV communication scenarios pose challenges for traditional centralized learning. To address these issues, this work presents BANCO-FL, a balanced and optimized federated learning framework combining a lightweight neural network with adaptive aggregation methods, FedAdam, FedMedian, and ClusterAvg. Experiments conducted on a real-world UAV dataset containing 2.35 million communication records demonstrate that BANCO-FL achieves a peak accuracy of 99.98%, 99.98% precision, 99.98% recall, and a 99.98% F1-score in 3-client and 9-client non-IID scenarios. Compared to standard baselines, BANCO-FL reduces misclassification rates by over 35%, improves training stability, and enhances fairness across clients. These findings show that BANCO-FL is a practical, scalable, and communication-efficient solution for real-world UAV anomaly detection.