Trustworthy and Robust Artificial Intelligence – Project Page

Duration: 06/2023 – 05/2027
Identifier: 01IS23027A


TRAIN is one of the few strategic projects selected in 2022 by ANR-BMBF part of German-French joint call for proposals on Artificial Intelligence – Edition 2022. The goal of TRAIN is to explore two main barriers to the widespread deployment of AI: the lack of trustworthiness and the lack of robustness. TRAIN, comprising of Inria (French Coordinator) and EURECOM from France and the Chair for Information Security at RUB (German Coordinator) and Fraunhofer IPT from Germany, will design, develop and evaluate new AI solutions that address these two challenges. The development of such AI systems requires careful attention at every aspect of the model development pipeline, starting from construction and training of models all the way to evaluation and deployment. The proposed research agenda focuses on the more challenging federated learning (FL) setting as a key paradigm to enable trustworthiness in the training phase. We also investigate trustworthiness and robustness of the evaluation and deployment phase in both centralized and collaborative machine learning. TRAIN solutions will be integrated into real-world frameworks in two main application domains: healthcare and smart industry.

Project Partners






Kick-off meeting in Sophia Antipolis (05.10.23 - 06.10.23)


  • Pascal Zimmer, Sebastien Andreina, Giorgia Marson, Ghassan Karame,
    Closing the Gap: Achieving Better Accuracy-Robustness Tradeoffs Against Query-Based Attacks,
    To appear in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024 [ Preprint ]