Federated Learning in Named Entity Recognition

Student: Luboshnikov Efim

Educational Programme: Financial Technology and Data Analysis (Master)

Final Grade: 10

Year of Graduation: 2020

This article is devoted to the implementation of the federated approach to named entity recognition. The classic BiLSTM-CNNs-CRF and its modifications are taken as baseline one-instance models. Federated training is conducted for them. Influence of use of pretrained embedding, use of various blocks of neural architecture on federated learning process and quality of final model is considered. Besides, other important questions arising in practice are considered and solved, for example, creation of distributed private dictionaries, selection of base model for federated learning and initialization of local optimizer.

Full text (added June 2, 2020)

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