In short




2020-06-01 > 2022-11-30






This project proposes an innovative Digital Information Management (DIM) concept, i.e. the AICHAIN solution, that aims at enabling the cyber-secured exploitation of large private data sets that belong to different stakeholders and that contain valuable information for ATM operations. To overcome the stakeholders’ reluctance to share sensitive data, the exploitation is not being performed by exchanging the data itself but by articulating an advanced privacy-preserving federated learning architecture in which neither the training data nor the training model need to be exposed. This is possible thanks to the innovative combination of two emerging DIM technologies: Federated Machine Learning (FedML) and Blockchain technologies.

The potential benefits of the new proposed DIM concept is being explored through ATM research use cases related to advanced Demand Capacity Balancing (DCB) predictive models of the Network Manager (NM), whose prediction performance is expected to significantly improve thanks to the exploitation of relevant operational private data from Airspace Users. The accuracy of the new DCB predictive models augmented with real operational data accessed through the AICHAIN solution are being benchmarked against the machine learning models for DCB that are currently in use or under research by NM.

The project also addresses the exploration of governance and incentives mechanisms as part of the AICHAIN solution concept architecture, to facilitate the adoption of the concept and the alignment of interests of the key stakeholders (especially of the data owners). The design of advanced governance & incentives mechanisms, which could be implemented using the mechanism of “smart contracts” available in the toolset of Blockchain, complemented with a theoretical identification of data exploitation benefits and with discussions in workshops participated by external experts.


GTD Air Services


Nommon Solutions and Technologies

Scaleout Systems

Swiss International Air Lines

This project has received funding from the SESAR Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No 894162

European Union