Federated Learning Approach for Cross-Enterprise HR Analytics in Oracle HCM

Abstract

Human Resource (HR) analytics is a decision-making and strategic capability that assists organizations in managing workforce, talent acquisition, employee retention etc. using data-driven insights. In today's world Human Capital Management (HCM) platforms, like Oracle HCM Cloud, are entwined up to the core of every modern enterprise with global business units as workforce data is scattered wide across geographical boundaries. Yet, with the increasing adoption of HR analytics, arise significant issues about employee privacy, protecting sensitive data, ensuring compliance with regulatory requirements and sharing data across organizations. Traditional centralized analytics architectures also involve enterprises centralizing sensitive employee data into a shared repository, raising major privacy risks as well as governance issues. Federated Learning (FL) has shown to be a powerful machine learning paradigm for distributed model training without the need of exchanging raw data. Federated learning enables local model training, where sensitive workforce information does not get transmitted from one organization to another, but rather only the local models (parameters or gradients) are communicated with a centralized aggregation server. This angle is perfectly in line with recent privacy regulations like GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act), enterprise governance frameworks. Cooperative analytics using federated learning and Oracle Human Capital Management (Oracle HCM) systems creates great potential for new cross-enterprise HR analytics by enabling an organization to extract common intelligence while maintaining data privacy. Cross-Enterprise HR Analytics: A Federated Learning Framework for Oracle HCM The framework combines Oracle HCM Cloud modules, federated learning, privacy-preserving techniques, secure parameter aggregation and predictive analytics. The evaluation of the framework uses multiple HR analytics use cases such as predicting employee attrition, forecasting workforce performance, optimizing recruitment processes and analyzing talent mobility. However, comparative results show that federated learning delivers a predictiveness on par with centralized machine learning but much lower privacy and regulatory lability. Our results show that federated learning can improve predictive accuracy, increase collaboration between enterprises, lower data-sharing inhibitions and build trust in organizations. This work presents a framework that enhances current research and practical implementation of privacy-aware HR analytics in Oracle HCM ecosystems.