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Federated Named Entity Recognition

arXiv 2203.15101 ↗First posted 28 Mar 2022cs.CL cs.AIcited by 1

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Abstract (arXiv)

We present an analysis of the performance of Federated Learning in a paradigmatic natural-language processing task: Named-Entity Recognition (NER). For our evaluation, we use the language-independent CoNLL-2003 dataset as our benchmark dataset and a Bi-LSTM-CRF model as our benchmark NER model. We show that federated training reaches almost the same performance as the centralized model, though with some performance degradation as the learning environments become more heterogeneous. We also show the convergence rate of federated models for NER. Finally, we discuss existing challenges of Federated Learning for NLP applications that can foster future research directions.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 28 Mar 2022.

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25 of 36 references.

References · 25

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13 more
  1. 2021A Field Guide to Federated Optimizationcited by 312
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  7. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 42
  8. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  9. 2020FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learningcited by 24
  10. 2020Pretraining Federated Text Models for Next Word Predictioncited by 22
  11. 2019Two-stage Federated Phenotyping and Patient Representation Learningcited by 19
  12. 2021A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learningcited by 12
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  1. 2024Federated Incremental Named Entity Recognitioncited by 2