FedSecureNLP: an Advanced Federated Learning approach for privacy-preserving and secure Ecommerce NLP System
DOI:
https://doi.org/10.21015/vtcs.v14i1.2398Abstract
This research presents an advanced Federated Learning approach for privacy-preserving and secure Ecommerce NLP System. Recently, the amount of information is growing very fast, and its importance is also rising quickly. Today, businesses handle huge volumes of data, like emails, which are often key to how they run day to day. It's difficult to find useful information from all this data and keeping it safe is a big worry. Keeping data secure is very important for protecting the trust of an online store and making customers feel safe when they make purchases. Hence, in this paper we have developed a privacy-preserving and secure FedSecureNLP system for text summarization, sentiment analysis and question answering. Within the FL configuration, the local training text summarization, sentiment predication and question answering is performed autonomously on each node local dataset. The Bidirectional Encoder Representations from Transformers (BERT) tokenization is used in the model to create tokens and extract term from amazon review data, after that, hierarchical deep learning for text (HDLTex) is used to predict sentiment rating. The Deep learning network (DeepCNN) is then used to do extractive summarization. Random multimodal deep learning (RMDL) is used for QA prediction. The QA model has different training and testing steps in the Module. During the evaluation phase, pretrained RMDL process the input query and semantically analyzed and generates relevant response. In this case FCDVO is designed to adjust the HDLTex, Deep CNN, and RMDL hyperparameters. The precision, recall, F-measure and Root Mean Square Error (RMSE) of the System that presented were 0.088, 92.896%, 92.481% and 92.688% respectively
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