Federated Learning-Enabled Analysis of Digital Inequality and Inclusive Modeling for Native-AI Telecom Networks Using Social Mobility Data

Abstract

Artificial intelligence is becoming a native capability of telecom networks, which requires AI models to be trained across multiple administrative and service domains while preserving data sovereignty and trust. To support network-level digital inclusion and differentiated service provisioning, this paper proposes a federated learning-based mobile network digital inequality modeling framework that integrates social mobility data. First, heterogeneous multi-source data–including mobile network usage records, geo-temporal mobility traces, and socioeconomic indicators from different network or organizational domains–are collected to build an AI-ready data plane without exposing raw user data. Second, a distributed feature-engineering and training scheme is designed in which each participating domain locally trains a gradient-boosting decision tree model and contributes encrypted model updates to a secure aggregation procedure; differential privacy is applied to enhance AI model governance and regulatory compliance in multi-vendor/multi-tenant telecom environments. Third, a network-facing digital inequality assessment service is constructed to quantify access and usage gaps among population segments, so that intent-based management or policy-based resource allocation can target under-served groups. Experiments on five cities show that the proposed framework achieves a validation accuracy of 91.5%; low-income users consume less than 40% of the network usage time of high-income users and, when the privacy budget ε=2, the risk of data leakage is reduced by 73.2%. These results demonstrate that privacy-preserving, federated, and explainable AI can be embedded as a native capability of telecom networks to provide actionable analytics for digital inclusion policies.