Secure Workforce Performance Modeling with Federated Learning in Cloud-Based HR Systems

Abstract

The rapid adoption of cloud-based Human Resource (HR) systems has transformed how organizations monitor, evaluate, and optimize workforce performance. While data-driven performance modeling offers significant strategic advantages, it simultaneously introduces serious privacy, security, and compliance challenges, particularly when sensitive employee data is centrally aggregated. Federated Learning (FL) has emerged as a promising paradigm to address these challenges by enabling collaborative model training without direct data sharing. This paper presents a comprehensive framework for secure workforce performance modeling using federated learning in cloud-based HR environments. We analyze architectural design principles, security threats, system scalability, and learning efficiency under realistic enterprise conditions. Through experimental evaluation on distributed HR performance datasets, we demonstrate that federated models can achieve competitive predictive accuracy while significantly reducing privacy risks and improving resilience against cloud-based attacks. The results indicate that federated learning, when carefully integrated with secure cloud infrastructures, offers a practical and scalable solution for next-generation HR analytics.