Corpus ID: 208309864

Federated Learning for Ranking Browser History Suggestions

@article{Hartmann2019FederatedLF,
  title={Federated Learning for Ranking Browser History Suggestions},
  author={Florian Hartmann and Sunah Suh and A. Komarzewski and Tim D. Smith and Ilana Segall},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.11807}
}
  • Florian Hartmann, Sunah Suh, +2 authors Ilana Segall
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • Federated Learning is a new subfield of machine learning that allows fitting models without collecting the training data itself. Instead of sharing data, users collaboratively train a model by only sending weight updates to a server. To improve the ranking of suggestions in the Firefox URL bar, we make use of Federated Learning to train a model on user interactions in a privacy-preserving way. This trained model replaces a handcrafted heuristic, and our results show that users now type over… CONTINUE READING

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