  Skin cancer is the most common cancer type. Usually, patients with suspicion
of cancer are treated by doctors without any aided visual inspection. At this
point, dermoscopy has become a suitable tool to support physicians in their
decision-making. However, clinicians need years of expertise to classify
possibly malicious skin lesions correctly. Therefore, research has applied
image processing and analysis tools to improve the treatment process. In order
to perform image analysis and train a model on dermoscopic images data needs to
be centralized. Nevertheless, data centralization does not often comply with
local data protection regulations due to its sensitive nature and due to the
loss of sovereignty if data providers allow unlimited access to the data. A
method to circumvent all privacy-related challenges of data centralization is
Distributed Analytics (DA) approaches, which bring the analysis to the data
instead of vice versa. This paradigm shift enables data analyses - in our case,
image analysis - with data remaining inside institutional borders, i.e., the
origin. In this documentation, we describe a straightforward use case including
a model training for skin lesion classification based on decentralised data.
