  We present one-shot federated learning, where a central server learns a
global model over a network of federated devices in a single round of
communication. Our approach - drawing on ensemble learning and knowledge
aggregation - achieves an average relative gain of 51.5% in AUC over local
baselines and comes within 90.1% of the (unattainable) global ideal. We discuss
these methods and identify several promising directions of future work.
