SEAL: An Open, Auditable, and Fair Data Generation Framework for AI-Native 6G Networks

2026-04-02

AI Summary

This work addresses the scarcity of real-world data in AI-native 6G networks and the limitations of existing synthetic data generation methods, which often suffer from bias, lack of auditability, and regulatory non-compliance. To overcome these challenges, the authors propose ERCD—the first synthetic data framework that intrinsically embeds ethical principles and regulatory compliance into the generation pipeline. By integrating a modular architecture with a federated learning-based closed-loop feedback mechanism, ERCD ensures privacy preservation while achieving alignment between real and synthetic data distributions. The framework further incorporates fairness constraints, bias detection modules, and standardized audit trails. Experimental results demonstrate that ERCD significantly outperforms state-of-the-art approaches, producing high-quality, fair, and auditable synthetic data as validated by metrics including Fréchet Inception Distance, equalized odds, and classification accuracy.

Abstract

AI-native 6G networks promise to transform the telecom industry by enabling dynamic resource allocation, predictive maintenance, and ultra-reliable low-latency communications across all layers, which are essential for applications such as smart cities, autonomous vehicles, and immersive XR. However, the deployment of 6G systems results in severe data scarcity, hindering the training of efficient AI models. Synthetic data generation is extensively used to fill this gap; however, it introduces challenges related to dataset bias, auditability, and compliance with regulatory frameworks. In this regard, we propose the Synthetic Data Generation with Ethics Audit Loop (SEAL) framework, which extends baseline modular pipelines with an Ethical and Regulatory Compliance by Design (ERCD) module and a Federated Learning (FL) feedback system. The ERCD integrates fairness, bias detection, and standardized audit trails for regulatory mapping, while the FL enables privacy-preserving calibration using aggregated insights from real testbeds to close the reality-simulation gap. Results show that the SEAL framework outperforms existing methods in terms of Frechet Inception Distance, equalized odds, and accuracy. These results validate the framework's ability to generate auditable and bias-mitigated synthetic data for responsible AI-native 6G development.

Problem

• data scarcity
• synthetic data
• bias
• auditability
• regulatory compliance

Innovation

• Synthetic Data Generation
• Ethical and Regulatory Compliance by Design
• Federated Learning
• Bias Mitigation
• Auditable AI