## 1 Introduction  
We are witnessing the rise of data-driven medicine, which has attracted considerable interest in diagnosis, clinical decision making, or research and has the potential to fundamentally revolutionise the healthcare domain [1, 2, 3]. Especially in research, the availability of sufficiently large and reliable data sets, e.g., patient records or medical images, are decisive for the impact of treatments and clinical trials [4]. The demand for a vast amount of data is fueled even more by the application of AI methodologies playing an increasing role in the healthcare sector due to their learning and prediction capabilities [1; 5; 6].  
Nevertheless, the ongoing success comes along with new emerging challenges. One major challenge is the compliance with patients' rights and privacy regulations because of the sensitive nature of patient data [7]. Such sensitive patient information finds particular protection in the European GDPR (https://gdpreu, accessed on 18 February 2022) for example.  
While these regulations enhance data privacy, they hinder healthcare institutions from sharing their data since it poses the risk of losing sovereignty over the data instances [8]. Consequently, researchers are limited to the availability of the data within the institutional borders, which reduces the effectiveness of clinical outcomes. In consequence of these regulatory requirements and their effect on data-driven medicine, there is a need for privacy-preserving data analysis.  
Therefore, approaches for Distibuted Analytics (DA) have come into focus [7; 9; 10; 11; 12; 13; 14; 15]. The advantages of these approaches stem from a paradigm shift in current data analyses. Conventionally, from an abstract perspective, data is brought to the analysis by following a data centralisation approach. So, data is collected from different institutions and transferred to a central location. As mentioned above, this procedure is difficult to incorporate with present data protection regulations. DA, however, reverses this procedure by bringing the analysis to the data. Hence, data never leaves its origin and stays under the control of the data owner [12]. While DA represents a first step towards privacy-preserving analyses and forges connections between institutions, it invokes new research questions induced by its highly decentralised nature. Particularly in the past years, the rising trend of FAIR ([[https://www.go-fair.org/fair-principles/](https://www.go-fair.org/fair-principles/)]([https://www.go-fair.org/fair-principles/](https://www.go-fair.org/fair-principles/)), accessed on 18 February 2022). Data Management and the _FAIRification_ of digital assets have revealed several shortcomings of DA infrastructures. Although selected DA concepts have been theoretically evaluated with respect to their contribution to the FAIR Data, it is still lacking actual practical development towards this direction [12]. Besides these more abstract principles, common approaches often require advanced technical understanding and knowledge to connect the data holding institution with the DA network (so-called on-boarding). This often has an impact on the deployment time and in-depth support, which does not scale when the number of new sites increases from a few to tens or hundreds per week [15].