Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction
In this study, the research presents the Federated Learning Enhanced Multi-Layer Perceptron (Fed-MLP) Long Short-Term Memory that is suggested by the research. The research intends to use the LSTM networks extensively that are proficient in spatial dependence capturing and integrate them with the collaborative learning framework of Federated Learning in an endeavor to augment the predictive competency. In the first step, we gather stock market indices from various financial organizations, using CAC40 stocks as the index for the French stock market. To guarantee data consistency and quality, pre-processing methods including linear interpolation and Z-score normalization are used. There are two types of models for each of the three basic elements within the Fed-MLP–LSTM, namely, MLP for feature extraction and LSTM for sequence modeling. Institutionally, each refining institution trains a local MLP–LSTM on the corpus specific to their institution, with only the model parameters being transferred to a central server through Federated Learning. A global model is created and updated through repeated training and totaling of parameters while preserving privacy of the data going to each node. In the performance evaluation, quantitative measures like Root-Mean-Square Error (RMSE), and accuracy are seven used. Hypothesis testing shows that we have good evidence to support that the proposed Fed-MLP–LSTM outperforms the other methods with the lowest RMSE of 0. 0108 and 98.3% of accuracy with reference to their respective cocaine molecule target. The proposed method is implemented in python. This suggests that using Federated Learning along with MLP and LSTM as the components of this vector enhanced the function increasing its capacity and reliability in predicting the trends of stocks. In conclusion, the present study suggests a sound solution for effective and secure stock market forecasting in collaboration environments that can find its use in the financial domains and securities businesses.