mlsanityData to 14 Oct 2026

Applied Federated Learning: Improving Google Keyboard Query Suggestions

arXiv 1812.02903 ↗First posted 7 Dec 2018cs.LG stat.MLcited by 204

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Abstract (arXiv)

Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access to the underlying user data. We describe our observations in federated training, compare metrics to live deployments, and present resulting quality increases. In whole, we demonstrate how federated learning can be applied end-to-end to both improve user experiences and enhance user privacy.

Key facts. Cited by 204 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Dec 2018.

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Citing papers per year: 2021: 58; 2022: 59; 2023: 44; 2024: 22; 2025: 16; 2026: 5.

204 papers.

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  1. 2021A Field Guide to Federated Optimizationcited by 315
  2. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  3. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 185
  4. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  5. 2022Neurotoxin: Durable Backdoors in Federated Learningcited by 98
  6. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 85
  7. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 81
  8. 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
  9. 2021Eluding Secure Aggregation in Federated Learning via Model Inconsistencycited by 67
  10. 2023Federated Learning of Gboard Language Models with Differential Privacycited by 66
  11. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  12. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
38 more
  1. 2021Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learningcited by 52
  2. 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
  3. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 50
  4. 2023(Amplified) Banded Matrix Factorization: A unified approach to private trainingcited by 49
  5. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  6. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  7. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 43
  8. 2022A Survey on Heterogeneous Federated Learningcited by 42
  9. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  10. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  11. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  12. 2022No One Left Behind: Inclusive Federated Learning over Heterogeneous Devicescited by 37
  13. 2022Federated Minimax Optimization: Improved Convergence Analyses and Algorithmscited by 37
  14. 2021Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devicescited by 36
  15. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  16. 2021FedJAX: Federated learning simulation with JAXcited by 32
  17. 2023On the Convergence of Federated Averaging with Cyclic Client Participationcited by 31
  18. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  19. 2022Pretrained Models for Multilingual Federated Learningcited by 30
  20. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 29
  21. 2022On the Impossible Safety of Large AI Modelscited by 28
  22. 2021IPLS : A Framework for Decentralized Federated Learningcited by 26
  23. 2021FedFace: Collaborative Learning of Face Recognition Modelcited by 26
  24. 2021Resource-Efficient Federated Learningcited by 26
  25. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  26. 2022On the (In)security of Peer-to-Peer Decentralized Machine Learningcited by 23
  27. 2023BayBFed: Bayesian Backdoor Defense for Federated Learningcited by 23
  28. 2021EasyFL: A Low-code Federated Learning Platform For Dummiescited by 20
  29. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  30. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  31. 2021FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robotscited by 18
  32. 2021PFA: Privacy-preserving Federated Adaptation for Effective Model Personalizationcited by 18
  33. 2021Privacy enabled Financial Text Classification using Differential Privacy and Federated Learningcited by 18
  34. 2023Decentralized Learning Made Easy with DecentralizePycited by 18
  35. 2021Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learningcited by 17
  36. 2022Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneitycited by 17
  37. 2022FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learningcited by 17
  38. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17

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