Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning
This addresses privacy concerns for financial institutions handling sensitive text data, but it is incremental as it combines existing privacy techniques with standard NLP models.
The paper tackles the problem of privacy in financial text classification by integrating differential privacy and federated learning with transformer models like BERT and RoBERTa, achieving privacy-utility tradeoffs evaluated on the Financial Phrase Bank dataset.
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.