Privacy-Preserving Traffic Intelligence Framework Based on Federated Large Models for Cellular Network Management
Keywords:
Federated learning; large models; cellular network; traffic intelligence; privacy preservation; spatial-temporal prediction; edge computingAbstract
Cellular network management increasingly depends on high-resolution traffic intelligence to optimize resource allocation, predict congestion, and enable self-organizing capabilities. The granular data required for such analytics, however, encode sensitive user mobility and communication patterns, raising acute privacy concerns under regulations such as GDPR. Centralized machine learning approaches that aggregate raw traffic logs from distributed base stations amplify these risks, creating a tension between operational insight and fundamental data protection. This paper presents a comprehensive architectural study of a privacy-preserving traffic intelligence framework that integrates federated learning with large pre-trained spatial-temporal models, designed to operate across the hierarchical edge-to-cloud continuum of modern cellular infrastructures. The framework leverages parameter-efficient fine-tuning and secure aggregation protocols to ensure that raw traffic traces never leave operator premises, while differential privacy mechanisms offer formal privacy guarantees. We provide an interdisciplinary systems analysis that examines the interaction among model accuracy, communication efficiency, computational heterogeneity, and regulatory compliance. The discussion extends to structural trade-offs in deployment, including energy sustainability, fairness across geographically diverse service areas, robustness against adversarial manipulation, and the governance challenges introduced by large models in multi-stakeholder telecom environments. Rather than proposing a singular algorithmic novelty, the paper develops a system-level perspective that connects technical architecture with policy, scalability, and long-term operational viability, furnishing a conceptual foundation for the next generation of privacy-aware network intelligence.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
