  Federated learning is an emerging paradigm for decentralized training of
machine learning models on distributed clients, without revealing the data to
the central server. Most existing works have focused on horizontal or vertical
data distributions, where each client possesses different samples with shared
features, or each client fully shares only sample indices, respectively.
However, the hybrid scheme is much less studied, even though it is much more
common in the real world. Therefore, in this paper, we propose a generalized
algorithm, FedGraph, that introduces a graph convolutional neural network to
capture feature-sharing information while learning features from a subset of
clients. We also develop a simple but effective clustering algorithm that
aggregates features produced by the deep neural networks of each client while
preserving data privacy.
