Framework for Addressing Imbalanced Data in Aviation with Federated Learning

The aviation industry generates vast amounts of data across multiple stakeholders, but critical faults and anomalies occur rarely, creating inherently imbalanced datasets that complicate machine learning applications. Traditional centralized approaches are further constrained by privacy concerns and regulatory requirements that limit data sharing among stakeholders. This paper presents a novel framework for addressing imbalanced data challenges in aviation through federated learning, focusing on fault detection, predictive maintenance, and safety management. The proposed framework combines specialized techniques for handling imbalanced data with privacy-preserving federated learning to enable effective collaboration while maintaining data security. The framework incorporates local resampling methods, cost-sensitive learning, and weighted aggregation mechanisms to improve minority class detection performance. The framework is validated through extensive experiments involving multiple aviation stakeholders, demonstrating a 23% improvement in fault detection accuracy and a 17% reduction in remaining useful life prediction error compared to conventional models. Results show the enhanced detection of rare but critical faults, improved maintenance scheduling accuracy, and effective risk assessment across distributed aviation datasets. The proposed framework provides a scalable and practical solution for using distributed aviation data while addressing both class imbalance and privacy concerns, contributing to improved safety and operational efficiency in the aviation industry.