2019/11/26 by Florian Hartmann, Sunah Suh, Hartmann, Florian +7 · 1 citation
Computer Science · Mathematics · Social Sciences · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mobile Crowdsensing and Crowdsourcing #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1911.11807
arxiv created 2019/11/26 · openalex publication_date 2019/11/26 · arxiv updated 2019/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 half a character less to find what they are looking for. To be able to deploy our system to real users without degrading their experience during training, we design the optimization process to be robust. To this end, we use a variant of Rprop for optimization, and implement additional safeguards. By using a numerical gradient approximation technique, our system is able to optimize anything in Firefox that is currently based on handcrafted heuristics. Our paper shows that Federated Learning can be used successfully to train models in privacy-respecting ways.