In this article, we investigated the secrecy performance of a three-hop relay network system with Power Splitting (PS) and Energy Harvesting (EH). In the presence of one eavesdropper, a signal is transferred from source to destination with the help of a relay. The source signal transmits in full-duplex (FD) mood, jamming the relay transfer signals to the destination. The relay and source employ Time Switching (TS) and Energy Harvesting (EH) techniques to obtain the power from the power beacon. In this study, we compared the Secrecy Rate of two Cooperative Schemes, Amplify and Forward (AF) and Decode and Forward (DF), for both designed systems with the established EH and PS system. The Secrecy Rate was improved by 50.5% in the AF scheme and by 44.2% in the DF scheme between the relay and eavesdropper at 40 m apart for the proposed system in EH and PS. This simulation was performed using the Monto Carlo method in MATLAB.

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