Abstract

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.

Metadata

publication

arXiv preprint arXiv:2203.15101, 2022

year

2022

publication date

2022/3/28

authors

Joel Mathew, Dimitris Stripelis, José Luis Ambite

link

https://arxiv.org/abs/2203.15101

resource_link

https://arxiv.org/pdf/2203.15101

journal

arXiv preprint arXiv:2203.15101