  Dimensionality Reduction is a commonly used element in a machine learning
pipeline that helps to extract important features from high-dimensional data.
In this work, we explore an alternative federated learning system that enables
integration of dimensionality reduced representations of distributed data prior
to a supervised learning task, thus avoiding model sharing among the parties.
We compare the performance of this approach on image classification tasks to
three alternative frameworks: centralized machine learning, individual machine
learning, and Federated Averaging, and analyze potential use cases for a
federated learning system without model sharing. Our results show that our
approach can achieve similar accuracy as Federated Averaging and performs
better than Federated Averaging in a small-user setting.
