Towards new privacy-preserving learning methods in renewable-dominated energy systems

Jean-François Toubeau is a post-doctoral researcher of the Fund for Scientific Research (F.R.S.–FNRS) at the University of Mons.

Eager to leverage artificial intelligence for improving the operation of modern energy systems, his research lies at the crossroad of Machine Learning and optimization under uncertainties.

His current research works focus on the development of new privacy-preserving  Machine Learning algorithms.

The privacy protection is achieved through two main pillars. First, federated learning is used to ensure that local measurements are not exchanged throughout learning and test procedures. Second, the learning is augmented with differential privacy, which injects calibratednoise to offer formal guarantees that the trained model cannot bereversed-engineered, thus reducing exposure to adversarial attacks.

This project, developed in collaboration with Prof. Yi Wang (The University of Hong Kong), received the IIF-SAS award (10,000$) annually attributed by the International Institute of Forecasters.

The success of our energy transition strongly relies on the propercoordination between stakeholders that need to team up to unlock the full potential of their individual resources. By collaboratively training a generic Machine Learning model, the value of personal data is unlocked(e.g., by capturing the explanatory power contained in all measurements), which is key to improve performance, and thus enhancethe satisfaction and engagement of all stakeholders.

This is an essential step towards an improved management of modern energy systems, which is key to reduce overall energy costs, while ensuring higher security of supply.

To learn more about his work, visit ResearchGate.

 

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