ArticleScientific reports2026
A new adaptive federated learning approach for privacy preserving UAV anomaly detection under non-IID distributions.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A federated learning-benchmarking framework for privacy-preserving UAV intrusion detection using adaptive aggregation algorithms.Scientific reports · 2026Article
- Residual-aware lightweight deep learning framework for high-fidelity intrusion detection in UAV swarm networks.Scientific reports · 2026Article
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Authors and funding
3 authors.
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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.
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