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.
Later, REF proposed oneshot Federated Learning (FL) in which the master node learns the model after a single round of communication.
One-Shot Federated Learning
ArXiv
ArXiv
Mathematics Computer Science