  With the popularization of AI solutions for image based problems, there has
been a growing concern for both data privacy and acquisition. In a large number
of cases, information is located on separate data silos and it can be difficult
for a developer to consolidate all of it in a fashion that is appropriate for
machine learning model development. Alongside this, a portion of these
localized data regions may not have access to a labelled ground truth. This
indicates that they have the capacity to reach conclusions numerically, but are
not able to assign classifications amid a lack of pertinent information. Such a
determination is often negligible, especially when attempting to develop image
based solutions that often necessitate this capability. With this being the
case, we propose an innovative vertical federated learning (VFL) model
architecture that can operate under this common set of conditions. This is the
first (and currently the only) implementation of a system that can work under
the constraints of a VFL environment and perform image segmentation while
maintaining nominal accuracies. We achieved this by utilizing an FCN that
boasts the ability to operate on federates that lack labelled data and
privately share the respective weights with a central server, that of which
hosts the necessary features for classification. Tests were conducted on the
CamVid dataset in order to determine the impact of heavy feature compression
required for the transfer of information between federates, as well as to reach
nominal conclusions about the overall performance metrics when working under
such constraints.
