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Privacy Preserving Machine Learning for Industrial Applications

PRIMAL will enable industrial deep learning applications by increasing the amount of usable data sources, by developing privacy preserving deep transfer learning methods. This allows utilizing data from even commercially competing parties, while by means of transfer and multi-task learning data from different (but related) sources can be leveraged. The result will be a software framework providing algorithms and interfaces to build privacy preserving predictive analytics applications for a wide range of (industrial) applications, exemplified in the independent areas of intralogistics, welding technology and bioinformatics.

Further Information

This project is funded by the FFG.