Big Data Analytics with Federated Learning for Privacy-Preserving Distributed Intelligence Systems

Abstract

Big data analytics has now become an essential part of the modern artificial intelligence, allowing intelligent decision-making in the areas of healthcare, finance, smart cities, and industrial automation. This is however not true because the traditional centralized machine learning methods usually force the collection and storage of user data in centralized servers in large quantities and hence the consideration of serious privacy, security, and data leakage issues. These problems are further increased by distributed intelligence systems, which represent the unstopping exchange of sensitive data between several devices and computing nodes. In order to solve these problems, this study will offer a privacy-sensitive federated learning model of a distributed big data analytics system. The suggested structure will allow several remote customers to jointly learn a worldwide machine learning model without exchanging crude local information, which reduces privacy safeguarding and decreases data reliance on central spots. The techniques of machine learning that are included in the study are distributed neural network training, secure parameter aggregation, and data preprocessing mechanisms to enhance the efficiency and scalability of the models. The performance of the suggested framework is analyzed based on conventional machine learning performance indicators such as Accuracy, Precision, Recall, and F1-Score. Through experimental analysis, the proposed federated learning model is shown to attain greater predictive performance in a distributed intelligence system, without compromising data privacy and communication efficiency. The study provides a scalable and secure privacy-aware big data analytics solution, and outlines the promise of federated learning in the next-generation intelligent system. The future work would be to incorporate blockchain and improved encryption techniques and optimization of the edge AI to achieve even better security and scalability.