  Federated learning enables multiple parties to collaboratively learn a model
without exchanging their data. While most existing federated learning
algorithms need many rounds to converge, one-shot federated learning (i.e.,
federated learning with a single communication round) is a promising approach
to make federated learning applicable in cross-silo setting in practice.
However, existing one-shot algorithms only support specific models and do not
provide any privacy guarantees, which significantly limit the applications in
practice. In this paper, we propose a practical one-shot federated learning
algorithm named FedKT. By utilizing the knowledge transfer technique, FedKT can
be applied to any classification models and can flexibly achieve differential
privacy guarantees. Our experiments on various tasks show that FedKT can
significantly outperform the other state-of-the-art federated learning
algorithms with a single communication round.
