Trustworthy Federated Data Analytics (TFDA)

To solve future grand challenges, data, computational power and analytics expertise need to be brought together at unprecedented scale. The need for data has become even larger in the context of recent advances in machine learning. Therefore, data-centric digital systems commonly exhibit a strong tendency towards centralized structures. While data centralization can greatly facilitate analysis, it also comes with several intrinsic disadvantages and threats not only from a technical but more importantly also from a legal, political and ethical perspective. Rooting in sophisticated security or trust requirements, overcoming these issues is cumbersome and time consuming. As a consequence, many research projects are substantially hindered, fail or are simply not addressed. In this interdisciplinary project we aim at facilitating the implementation of decentralized, cooperative data analytics architectures within and beyond Helmholtz by addressing the most relevant issues in such scenarios. Trustworthy Federated Data Analytics (TFDA) will facilitate bringing the algorithms to the data in a trustworthy and regulatory compliant way instead of going a data-centric way. TFDA will address the technical, methodical and legal aspects when ensuring trustworthiness of analysis and transparency regarding the analysis in- and outputs without violating privacy constraints. To demonstrate applicability and to ensure the adaptability of the methodological concepts, we will validate our developments for the usage in medical research with the use case “Federated radiation therapy study” before disseminating the results.

Participating Institutions


The CISPA Helmholtz Center for Information Security (CISPA) in Saarbrücken is one of the world’s leading research institutions in information security and privacy, with a dedicated focus on addressing the grand research challenges in security and privacy in a comprehensive and holistic manner, in particular the intersection of security and privacy with AI/machine learning. It strives for cutting-edge, often disruptive foundational research, augmented with innovative application-oriented research, corresponding technology transfer and societal outreach. Medical security and privacy, as well as foundational research in AI/machine learning have been topics of central importance for CISPA ever since its inauguration.

The German Cancer Research Center (DKFZ) is one of the world's leading cancer research centers and the largest cancer research center in Europe. The DKFZ conducts research on the investigation of the mechanisms underlying cancer, the identification of cancer risk factors, and ultimately on trying to come up with approaches to prevent people from getting cancer in the first place. Moreover, novel approaches are being developed to make tumor diagnosis more precise and treatment of cancer patients more successful. DKFZ research is strongly supported by data-driven approaches and relies crucially on modern AI/ML technologies.