Federated Learning–Enabled Privacy-Preserving Intelligent Systems for Distributed Data Environments

Abstract

The intensive pace of the growth of data-intensive intelligent systems has enhanced the issues over privacy, data ownership and regulatory compliance. Traditional centralized machine learning systems presuppose the transfer of vast amounts of data, which is sensitive and vulnerable to security risks and liability to centralized servers, making systems prone to security breaches and legal liability. Federated Learning (FL) has recently become a decentralized framework of machine learning that allows collaborative training of model on distributed data in a setting that does not require the sharing of data. The research paper is a proposal of a federated learning-enabled intelligent system architecture that facilitates privacy-sensitive, scalable, and communication-efficient learning. The architecture incorporates distributed client training, secure aggregation and adaptive optimization solutions. An extensive simulation-based analysis is done in terms of accuracy, convergence behavior, communication overhead, and exposure to privacy. The experimental outcomes show that the suggested FL-based system is quite accurate as well as the centralized learning and can significantly decrease communication costs as well as the appearance of raw data. The results make federated learning a strong base of the next generation of privacy-conscientious smart systems in the fields of healthcare, finance, and massive IoT networks.