Privacy-Preserving Performance Analysis in Cloud HR Systems Using Federated Learning

Abstract

The rapid digital transformation of human resource (HR) management has led organizations to increasingly rely on cloud-based platforms for employee performance analysis. While these systems offer scalability, accessibility, and advanced analytics, they also introduce significant privacy risks due to the centralized collection of sensitive employee data. Traditional performance analytics frameworks often require aggregating personal and behavioral data into a single cloud repository, increasing vulnerability to data breaches, insider threats, and regulatory non-compliance. These challenges have become particularly pronounced as organizations operate across jurisdictions with strict data protection laws. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training without requiring raw data to leave local environments. Instead of centralizing employee records, FL allows distributed HR systems to train shared performance models while retaining sensitive information at the source. This approach aligns naturally with privacy-by-design principles and offers a practical path toward trustworthy analytics in cloud HR ecosystems. This paper proposes a federated learning–based framework for privacy-preserving performance analysis in cloud HR systems.