Computer Science > Machine Learning
[Submitted on 28 Feb 2019 (v1), last revised 5 Mar 2019 (this version, v2)]
Title:One-Shot Federated Learning
View PDFAbstract: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.
Submission history
From: Neel Guha [view email][v1] Thu, 28 Feb 2019 15:55:18 UTC (282 KB)
[v2] Tue, 5 Mar 2019 20:33:39 UTC (282 KB)
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