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Federated Named Entity Recognition
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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.
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Cited by 1 Pith paper
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Federated Incremental Named Entity Recognition
A new federated incremental NER setup is introduced, along with a LGFD model using structural distillation, contrastive loss, and an entropy-based task switch detector that improves average F1 over existing INER baselines.
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