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A platform for privacy-preserving Federated Machine Learning using Blockchain to enable Operational Improvements in ATM

Project description

Improving air traffic management through machine learning collaboration on private data sets

Air traffic management (ATM) can greatly benefit from cyber-secured exploitation of large private data sets belonging to different stakeholders. Currently, however, there is a reluctance to share sensitive data. The EU-funded AICHAIN project is proposing an innovative digital information management (DIM) concept that will help exploit those valuable private datasets. It will combine two emerging DIM technologies, federated machine learning and blockchain, to articulate an advanced privacy-preserving federated learning design in which exchanging data and training models will not be compromised. It will also investigate the potential benefits of the innovative DIM through ATM research cases related to the advanced demand capacity balancing prognostic model of the network manager augmented with real operational data from airspace users.

Objective

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 will not be 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 will be 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 will be 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 will be benchmarked against the machine learning models for DCB that are currently in use or under research by NM.

The project will also address 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, will be complemented with a theoretical identification of data exploitation benefits and with discussions in workshops participated by external experts.

Coordinator

SITA EWAS APPLICATION SERVICES SL
Net EU contribution
€ 240 907,16
Address
CALLE DE PALLARS 193-205 PLANTA 11
08005 Barcelona
Spain

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Region
Este Cataluña Barcelona
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost
€ 283 407,16

Participants (6)