Cloud HR Performance Analytics Enabled by Federated Learning

Abstract

Cloud-based Human Resource (HR) platforms have become central to modern workforce management, enabling organizations to collect, process, and analyze employee performance data at scale. While these platforms offer unprecedented analytical capabilities, they also introduce significant privacy, security, and compliance challenges due to the sensitive nature of employee information. Traditional centralized analytics architectures often require raw data aggregation, increasing the risk of data breaches and regulatory violations. Federated Learning (FL) emerges as a compelling paradigm that allows collaborative model training across distributed HR data sources without exposing raw employee records. This paper presents a comprehensive federated learning–enabled framework for cloud HR performance analytics, emphasizing privacy preservation, scalability, and resilience. Through a detailed system design, experimental evaluation, and performance analysis, we demonstrate how federated learning can achieve high analytical accuracy while maintaining strict data locality constraints. The findings suggest that FL-based HR analytics not only improve trust and compliance but also enhance the robustness and sustainability of cloud HR ecosystems.