NASA, DoD, and commercial aircraft operators need to transform vast amounts of aircraft data accumulated in distributed databases into actionable knowledge. We propose distributed algorithms for data-driven health monitoring on aircraft, aircraft fleet, and national airspace levels. The proposed algorithms are based on distributed optimization formulation, and, unlike existing distributed processing methods, have rigorous guarantees of producing the same results as centralized processing would do. Our algorithms will be implemented in an open scalable framework that allows integrating distributed data and federated third party algorithms for anomaly detection, diagnosis, prediction, and prognosis. We will apply the proposed approach to aircraft performance monitoring from FOQA data. We will train regression models of aircraft performance using distributed agents associated with different data sets, locations, and organizations. The trained models will be then used for anomaly detection, diagnosis (fault isolation), prognosis (forecasting), and mitigation (decision support). This project will develop web-based distributed open architecture software implementing the proposed optimization-based approaches and demonstrate scalability to at least 10 TB of data. Besides the developed algorithms, we will explore integration of third party algorithms into the distributed environment. The developed technologies will be applicable to a broad range of aircraft-related and other problems.