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Federated Learning is a new subfield of machine learning that allows fittingmodels 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 ofFederated Learning to train a model on user interactions in aprivacy-preserving way. This trained model replaces a handcrafted heuristic,and our results show that users now type over half a character less to findwhat they are looking for. To be able to deploy our system to real userswithout degrading their experience during training, we design the optimizationprocess to be robust. To this end, we use a variant of Rprop for optimization,and implement additional safeguards. By using a numerical gradientapproximation technique, our system is able to optimize anything in Firefoxthat is currently based on handcrafted heuristics. Our paper shows thatFederated Learning can be used successfully to train models inprivacy-respecting ways.

Federated Learning for Ranking Browser History Suggestions