Name
Abstract | ||
|---|---|---|
Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access to the underlying user data. We describe our observations in federated training, compare metrics to live deployments, and present resulting quality increases. In whole, we demonstrate how federated learning can be applied end-to-end to both improve user experiences and enhance user privacy. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1812.02903 | 6 | 0.45 |
References | Authors | |
7 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Timothy Yang | 1 | 6 | 0.45 |
| Galen Andrew | 2 | 636 | 37.94 |
| Hubert Eichner | 3 | 27 | 1.56 |
| Haicheng Sun | 4 | 6 | 0.79 |
| Wei Li | 5 | 396 | 76.68 |
| Nicholas Kong | 6 | 6 | 0.79 |
| Daniel Ramage | 7 | 2109 | 93.77 |
| Françoise Beaufays | 8 | 6 | 2.82 |