An LLM-Powered Framework for Privacy-Preserving and Scalable Labor Market Analysis

Timely and reliable labor market intelligence is crucial for evidence-based policymaking, workforce planning, and economic forecasting. However, traditional data collection and centralized analytics raise growing concerns about privacy, scalability, and institutional data governance. This paper presents a large language model (LLM)-powered framework for privacy-preserving and scalable labor market analysis, designed to extract, structure, and interpret occupation, skill, and salary information from distributed textual sources. Our framework integrates domain-adapted LLMs with federated learning (FL) and differential privacy (DP) to enable collaborative model training across organizations without exposing sensitive data. The architecture employs secure aggregation and privacy budgets to prevent information leakage during parameter exchange, while maintaining analytical accuracy and interpretability. The system performs multi-task inference—including job classification, skill extraction, and salary estimation—and aligns outputs to standardized taxonomies (e.g., SOC, ISCO, ESCO). Empirical evaluations on both public and semi-private datasets demonstrate that our approach achieves superior performance compared to centralized baselines, while ensuring compliance with privacy and data-sharing regulations. Expert review further confirms that the generated trend analyses are accurate, explainable, and actionable for policy and research. Our results illustrate a practical pathway toward decentralized, privacy-conscious, and large-scale labor market intelligence.