One-Shot Federated Learning

2019-02-28 · Neel Guha, Ameet Talwalkar, Virginia Smith

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.

📄 PDF Abstract BibTeX arXiv:1902.11175

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