mlsanityData to 14 Oct 2026

Federated Learning Of Out-Of-Vocabulary Words

arXiv 1903.10635 ↗First posted 26 Mar 2019cs.CLcited by 51

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Abstract (arXiv)

We demonstrate that a character-level recurrent neural network is able to learn out-of-vocabulary (OOV) words under federated learning settings, for the purpose of expanding the vocabulary of a virtual keyboard for smartphones without exporting sensitive text to servers. High-frequency words can be sampled from the trained generative model by drawing from the joint posterior directly. We study the feasibility of the approach in two settings: (1) using simulated federated learning on a publicly available non-IID per-user dataset from a popular social networking website, (2) using federated learning on data hosted on user mobile devices. The model achieves good recall and precision compared to ground-truth OOV words in setting (1). With (2) we demonstrate the practicality of this approach by showing that we can learn meaningful OOV words with good character-level prediction accuracy and cross entropy loss.

Key facts. Cited by 51 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Mar 2019.

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Citing papers per year: 2021: 15; 2022: 13; 2023: 13; 2024: 7; 2025: 2; 2026: 1.

51 papers.

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  1. 2021A Field Guide to Federated Optimizationcited by 315
  2. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  3. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 43
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  8. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  9. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 21
  10. 2024NEO-BENCH: Evaluating Robustness of Large Language Models with Neologismscited by 20
  11. 2023FLINT: A Platform for Federated Learning Integrationcited by 17
  12. 2022Training Mixed-Domain Translation Models via Federated Learningcited by 12
38 more
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  4. 2022Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusioncited by 10
  5. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
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  7. 2022Pisces: Efficient Federated Learning via Guided Asynchronous Trainingcited by 8
  8. 2021FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learningcited by 7
  9. 2023Federated Online and Bandit Convex Optimizationcited by 7
  10. 2023Monitoring and Adapting ML Models on Mobile Devicescited by 6
  11. 2024On the Byzantine-Resilience of Distillation-Based Federated Learningcited by 5
  12. 2021Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradientcited by 4
  13. 2021Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environmentscited by 4
  14. 2022Multi-Model Federated Learningcited by 4
  15. 2022Training a Tokenizer for Free with Private Federated Learningcited by 4
  16. 2022Reconciling Security and Communication Efficiency in Federated Learningcited by 4
  17. 2022A Convergence Theory for Federated Average: Beyond Smoothnesscited by 4
  18. 2024Confidential Federated Computationscited by 4
  19. 2022LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximationcited by 3
  20. 2022Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignmentcited by 3
  21. 2022FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Taskscited by 3
  22. 2021Aggregation Delayed Federated Learningcited by 2
  23. 2022Robust Quantity-Aware Aggregation for Federated Learningcited by 2
  24. 2023Federated Automatic Differentiationcited by 2
  25. 2023Now It Sounds Like You: Learning Personalized Vocabulary On Devicecited by 2
  26. 2023Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verificationcited by 2
  27. 2023Dynamically Weighted Federated k-Meanscited by 2
  28. 2024Contrastive Federated Learning with Tabular Data Siloscited by 2
  29. 2021Preliminary Steps Towards Federated Sentiment Classificationcited by 1
  30. 2021Secure Bayesian Federated Analytics for Privacy-Preserving Trend Detectioncited by 1
  31. 2023Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigationscited by 1
  32. 2026Distributed Online Convex Optimization with Compressed Communication: Optimal Regret and Applicationscited by 1
  33. 2022Privacy-Preserving Online Content Moderation: A Federated Learning Use Casecited by 0
  34. 2023FedPH: Privacy-enhanced Heterogeneous Federated Learningcited by 0
  35. 2023Towards Sybil Resilience in Decentralized Learningcited by 0
  36. 2024DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Servicecited by 0
  37. 2024Personalized Federated Learning for Statistical Heterogeneitycited by 0
  38. 2025Accelerating MoE Model Inference with Expert Shardingcited by 0

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