Intelligent Employee Performance Analytics via Federated Learning in Cloud HR Platforms

Abstract

The rapid digitization of human resource management has transformed cloud-based HR platforms into critical repositories of employee performance data, behavioral indicators, and organizational productivity signals. While these platforms enable data-driven decision-making at scale, they also introduce significant privacy, security, and compliance challenges due to the sensitive nature of employee information. Centralized analytics models often require aggregating raw performance data across departments or geographical locations, increasing exposure to data leakage, insider threats, and regulatory violations. This paper proposes an intelligent employee performance analytics framework that leverages federated learning to enable collaborative model training across distributed cloud HR systems without centralizing sensitive data. By integrating privacy-preserving learning mechanisms with scalable cloud architectures, the proposed approach balances analytical accuracy with data confidentiality. Experimental evaluations demonstrate that federated performance analytics achieves comparable predictive accuracy to centralized models while substantially reducing privacy risk and improving system resilience. The findings highlight federated learning as a practical and future-ready paradigm for intelligent performance management in modern cloud HR platforms.