Secure Collaborative Model Training with Dynamic Federated Learning in Multi-Domain Environments

According to the European Union Aviation Safety Agency (EASA), AI-based algorithms, combined with extensive fleet data, could enable early detection of potential engine failures, leading to proactive predictive maintenance in air travel. At a global level, the Independent Data Consortium for Aviation (IDCA) recognizes the potential of collaborative data sharing in the airline industry. However, data ownership-related issues, such as privacy, intellectual property, and regulatory compliance, pose significant obstacles to realizing the vision of combining fleet data to improve predictive maintenance algorithms. In this paper, we use NASA's Turbofan Jet Engine Dataset (N-CMAPSS) to demonstrate how airlines could leverage the power of Federated Learning (FL) and microservices, to collaboratively train a global Machine-Learning (ML) model that can enable airline companies to utilize their data for predictive maintenance, while maintaining control.