Data to 7 Oct 2026

Federated Learning for Mobile Keyboard Prediction

arXiv 1811.03604 ↗First posted 8 Nov 2018cs.CLcited by 565

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Abstract (arXiv)

We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones. Server-based training using stochastic gradient descent is compared with training on client devices using the Federated Averaging algorithm. The federated algorithm, which enables training on a higher-quality dataset for this use case, is shown to achieve better prediction recall. This work demonstrates the feasibility and benefit of training language models on client devices without exporting sensitive user data to servers. The federated learning environment gives users greater control over the use of their data and simplifies the task of incorporating privacy by default with distributed training and aggregation across a population of client devices.

Key facts. Cited by 565 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2018.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 124; 2022: 143; 2023: 110; 2024: 95; 2025: 60; 2026: 31.

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2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
2021A Field Guide to Federated Optimizationcited by 312
2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 185
2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 184
2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 133
2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 127
2022Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Datasetcited by 113

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2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
2022Neurotoxin: Durable Backdoors in Federated Learningcited by 97
2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
2024A Survey of Resource-efficient LLM and Multimodal Foundation Modelscited by 84
2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
2021Federated Reconstruction: Partially Local Federated Learningcited by 78
2022Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakagecited by 72
2022Recovering Private Text in Federated Learning of Language Modelscited by 67
2021Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacycited by 66
2021The Skellam Mechanism for Differentially Private Federated Learningcited by 66
2021Eluding Secure Aggregation in Federated Learning via Model Inconsistencycited by 66
2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 66
2023TARGET: Federated Class-Continual Learning via Exemplar-Free Distillationcited by 64
2023Federated Learning of Gboard Language Models with Differential Privacycited by 64
2022FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learningcited by 62
2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
2022Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streamscited by 59
2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
2021Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learningcited by 52
2022Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Modelscited by 52
2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
2021Federated Learning: A Signal Processing Perspectivecited by 49
2023(Amplified) Banded Matrix Factorization: A unified approach to private trainingcited by 49
2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
2021SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsificationcited by 46
2023Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscapecited by 46
2021SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Trainingcited by 45
2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
2021Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrixcited by 43
2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
2021Federated Learning Meets Natural Language Processing: A Surveycited by 42
2023A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Taskscited by 41
2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40

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