Privacy is important considering the financial Domain as such data is highly
confidential and sensitive. Natural Language Processing (NLP) techniques can be
applied for text classification and entity detection purposes in financial
domains such as customer feedback sentiment analysis, invoice entity detection,
categorisation of financial documents by type etc. Due to the sensitive nature
of such data, privacy measures need to be taken for handling and training large
models with such data. In this work, we propose a contextualized transformer
(BERT and RoBERTa) based text classification model integrated with privacy
features such as Differential Privacy (DP) and Federated Learning (FL). We
present how to privately train NLP models and desirable privacy-utility
tradeoffs and evaluate them on the Financial Phrase Bank dataset.

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