Federated Learning and Privacy-Preserving AI: Securing Data Without Centralization

Abstract

The article examines the convergence between federated learning and privacy-preserving AI, focusing on their role in providing secure and decentralized data processing. Federated learning is a framework that allows multiple devices to train a model together without centralizing sensitive information, thus avoiding privacy issues and benefiting the improvement of AI models. These technologies make use of privacy-preserving methods (including differential privacy and homomorphic encryption) to guarantee confidentiality of their data during the machine learning stage. The paper describes how federated learning is applied in cybersecurity and highlights its relevance in protecting data and legal requirements. Such goals encompass showing how federated learning will reduce the risks linked to the conventional centralized data setups and enhance privacy in practical use cases. The article presents successful applications of these technologies in sectors like healthcare and finance, among others, through case studies that provide insight into their practical benefits and challenges. The results show the potential of federated learning as a powerful tool to improve cybersecurity and protect user data.