# Federated Learning for Ranking Browser History Suggestions

arXiv: https://arxiv.org/abs/1911.11807 · HTML: https://mlsanity.com/paper/1911.11807/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 24 of 24)

- [Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning](https://mlsanity.com/paper/2203.09249/) (2022)
- [Resource-Efficient Federated Learning](https://mlsanity.com/paper/2111.01108/) (2021)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots](https://mlsanity.com/paper/2101.03705/) (2021)
- [FLINT: A Platform for Federated Learning Integration](https://mlsanity.com/paper/2302.12862/) (2023)
- [Training Mixed-Domain Translation Models via Federated Learning](https://mlsanity.com/paper/2205.01557/) (2022)
- [A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things](https://mlsanity.com/paper/2104.10501/) (2021)
- [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://mlsanity.com/paper/2206.09264/) (2022)
- [GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning](https://mlsanity.com/paper/2212.01523/) (2022)
- [OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework](https://mlsanity.com/paper/2109.07852/) (2021)
- [FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning](https://mlsanity.com/paper/2206.03852/) (2022)
- [An Optimization Framework for Federated Edge Learning](https://mlsanity.com/paper/2111.13526/) (2021)
- ["You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning](https://mlsanity.com/paper/2206.12183/) (2022)
- [Evaluating Privacy Leakage in Split Learning](https://mlsanity.com/paper/2305.12997/) (2023)
- [Smart Multi-tenant Federated Learning](https://mlsanity.com/paper/2207.04202/) (2022)
- [Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling](https://mlsanity.com/paper/2402.10097/) (2024)
- [Federated Unbiased Learning to Rank](https://mlsanity.com/paper/2105.04761/) (2021)
- [Federated Learning -- Methods, Applications and beyond](https://mlsanity.com/paper/2212.11729/) (2022)
- [FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2307.08574/) (2023)
- [REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices](https://mlsanity.com/paper/2309.13643/) (2023)
- [FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection](https://mlsanity.com/paper/2407.00943/) (2024)
- [AdFL: In-Browser Federated Learning for Online Advertisement](https://mlsanity.com/paper/2602.06336/) (2026)
- [Joint Domain-Class Modeling for Federated Learning Under Feature Skew](https://mlsanity.com/paper/2609.22932/) (2026)
- [DeFiRank: Federated Pairwise Ranking for Governance in Decentralized Financial Networks](https://mlsanity.com/paper/2610.12916/) (2026)

## Abstract (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 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.