Federated Learning for Ranking Browser History Suggestions

Abstract

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.

References in corpus (5)

• Deep Learning with Differential Privacy
• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Learning: Strategies for Improving Communication Efficiency
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• Practical Secure Aggregation for Federated Learning on User-Held Data

Cited by in corpus (8)

• The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
• Oort: Efficient Federated Learning via Guided Participant Selection
• A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
• Federated Composite Optimization
• Federated Learning -- Methods, Applications and beyond
• OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
• Federated learning with differential privacy and an untrusted aggregator
• Federated Unbiased Learning to Rank