Data-Private Employee Analytics in Cloud HR Systems Through Federated Learning

Abstract

The rapid adoption of cloud-based Human Resource (HR) systems has enabled organizations to collect, process, and analyze employee performance data at unprecedented scale. While these systems enhance workforce planning, talent management, and productivity forecasting, they also introduce critical privacy and security concerns due to centralized data aggregation. Employee data, which often includes sensitive behavioral, productivity, and evaluation metrics, becomes an attractive target for breaches and misuse. This paper proposes a data-private employee analytics framework for cloud HR systems using Federated Learning (FL), where predictive models are trained collaboratively across distributed organizational nodes without transferring raw employee data. The proposed approach preserves privacy by design while maintaining analytical accuracy and scalability. We present a complete system architecture, learning methodology, and experimental evaluation using synthetic and real-world HR datasets. The results demonstrate that federated models achieve performance comparable to centralized approaches while significantly reducing data exposure risks. This study highlights how federated learning can serve as a foundational technology for trustworthy, scalable, and regulation-compliant employee analytics in modern cloud HR environments.