Multi-Objective Optimization in Federated Learning for Balancing Employee Privacy, Accuracy, and Organizational Utility

Abstract

The rise of data-driven decision-making in workforce management has made employee performance analytics an essential part of modern organizational ecosystems. However, the sensitive nature of employee data necessitates solutions that respect privacy while maintaining model accuracy and ensuring organizational utility. Federated learning (FL) has emerged as a promising paradigm that enables collaborative model training without centralized data aggregation, thus protecting employee confidentiality. Despite its potential, FL presents trade-offs between employee privacy, predictive accuracy, and the organization’s need for actionable insights. This research paper investigates multi-objective optimization (MOO) approaches within federated learning to balance these competing goals. Using a case study of federated workforce analytics, we analyze the integration of privacy-preserving mechanisms such as differential privacy with optimization frameworks that simultaneously account for accuracy and utility. Experimental results demonstrate that incorporating multi-objective optimization not only improves fairness in workforce assessment but also achieves a more sustainable balance among privacy, performance, and organizational value. The study provides theoretical and empirical insights, offering practical guidelines for deploying FL systems in sensitive employee evaluation environments.