Federated Learning with AI-Enabled Databases for Privacy-Preserving Analytics

Abstract

The rapid adoption of AI-enabled databases in various sectors has fueled the need for advanced techniques that balance data-driven innovation with privacy preservation. This paper explores the integration of federated learning with AI-enabled databases to facilitate privacypreserving analytics, allowing decentralized data processing while ensuring data confidentiality. By employing federated learning, sensitive data remains localized on individual devices or servers, and only model updates are shared, significantly reducing the risk of data breaches. The study investigates the challenges and opportunities of implementing federated learning in AI-powered databases, highlighting key areas such as model accuracy, communication efficiency, data heterogeneity, and privacy guarantees. Results from experimental simulations demonstrate the effectiveness of federated learning in maintaining both performance and privacy in diverse database environments. This research provides a foundation for developing secure, scalable, and efficient privacy-preserving analytics solutions using federated learning in AI-enabled database systems.